EP4308434A1 - Railway monitoring system and method - Google Patents

Railway monitoring system and method

Info

Publication number
EP4308434A1
EP4308434A1 EP22718958.6A EP22718958A EP4308434A1 EP 4308434 A1 EP4308434 A1 EP 4308434A1 EP 22718958 A EP22718958 A EP 22718958A EP 4308434 A1 EP4308434 A1 EP 4308434A1
Authority
EP
European Patent Office
Prior art keywords
component
railway
sensor
sensor data
physical property
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22718958.6A
Other languages
German (de)
French (fr)
Inventor
Thomas ZEHELEIN
Simon DESELAERS
Andres HERNANDEZ CANSECO
Olga SPACKOVA
Seyed Mohamed SEYEDTORABI
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Konux GmbH
Original Assignee
Konux GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Konux GmbH filed Critical Konux GmbH
Publication of EP4308434A1 publication Critical patent/EP4308434A1/en
Pending legal-status Critical Current

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
    • B61L27/53—Trackside diagnosis or maintenance, e.g. software upgrades for trackside elements or systems, e.g. trackside supervision of trackside control system conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning, or like safety means along the route or between vehicles or vehicle trains
    • B61L23/04—Control, warning, or like safety means along the route or between vehicles or vehicle trains for monitoring the mechanical state of the route
    • B61L23/042—Track changes detection
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning, or like safety means along the route or between vehicles or vehicle trains
    • B61L23/04—Control, warning, or like safety means along the route or between vehicles or vehicle trains for monitoring the mechanical state of the route
    • B61L23/042—Track changes detection
    • B61L23/048—Road bed changes, e.g. road bed erosion

Definitions

  • the invention lies in the field of railways monitoring and particularly in the field of analyzing physical properties of railway components. More particularly, the present invention relates to a system for analyzing physical properties of a railway network, a method performed in such a system and corresponding use of a system.
  • Rail, railway or rail transport has been developed for transferring goods and passengers on wheeled vehicles on rails, also known as tracks.
  • rail vehicles rolling stock
  • Tracks commonly consist of steel rails, installed on ties or sleepers and ballast, on which the rolling stock, usually provided with metal wheels, moves.
  • Other variations are also possible, such as slab track, where the rails are fastened to a concrete foundation resting on a subsurface.
  • Rolling stock in a rail transport system generally encounters lower frictional resistance than road vehicles, so passenger and freight cars (carriages and wagons) can be coupled into longer trains.
  • Power is provided by locomotives, which either draw electric power from a railway electrification system or produce their own power, usually by diesel engines. Most tracks are accompanied by a signaling system.
  • Railways are a safe land transport system when compared to other forms of transport. Additionally, railways are capable of high levels of passenger and cargo utilization and energy efficiency but are often less flexible and more capital-intensive than road transport, when lower traffic levels are considered.
  • Measurements of such sensors may be used to further measurements, control, prediction and optimization of operation of railways.
  • Kaewunruen et al. (DOI: 10.1016/j.ndteint.2007.03.004) discloses an approach to judge the dynamic responses, for example, frequency response functions, together with a visual inspection of existing field conditions.
  • Kaewunruen et a! relates to examples of testing results representing a deficient integrity and based on such results.
  • Lam et al. (DOI: 10.1016/j.proeng.2011.07.022) reports a feasibility study in the use of measured modal parameters of in-situ concrete sleepers to detect possible damage of the underlying railway ballast, wherein ballast damage is defined as ballast degradation and ballast cementation with the accumulation of fines.
  • Oregui et al. (ISMA2010) relates to an approach for detection and maintenance of tracks against squats and corrugation. In particular, it relates to a method to relate track parameters to squats and corrugation initiation and growth, and to a finite element model.
  • an object of the present invention to overcome or at least alleviate the shortcomings of the prior art. More particularly, it is an object of the present invention to provide a system and a method for analyzing physical properties of railway components comprised by the railway network.
  • the invention in a first aspect, relates to a system for monitoring physical properties of at least one railway component of a railway network, the system comprising: at least one sensor component configured to sample at least one sensor data relevant to the railway network, at least one processing component configured to process the at least one sensor data, at least one storing component configured to store the sensor, and at least one analyzing component.
  • the at least one analyzing component may be configured to receive the at least one sensor data from the at least one sensor component.
  • the system may comprise an excitation component configured to provide an excitation force to at least one railway component of the railway network. This is particularly advantageous, as characteristics of the excitation force are known beforehand, which results in an improvement in respect with other approaches, such as, systems configured to work with a train-induced excitation.
  • the excitation force may comprise an impulse force. In a further embodiment the excitation force may comprise a step force. In another embodiment, the excitation force may comprise a white noise. In a further embodiment, the excitation force may comprise a sinusoidal sweep. Additionally or alternatively, the excitation force may comprise a colored noise.
  • the excitation force may comprise a frequency range between 0 and 10,000 Hz, preferably between 0 and 8,000 Hz, more preferably between 0 and 2,000 Hz.
  • the at least one sensor component may be configured to measure at least one dynamic response to the excitation force of the at least one railway component of the railway network.
  • the system may further be configured to identify at least one characteristic eigenfrequency of the at least one railway component. This is particularly advantageous, as a location of the at least one eigenfrequency, i.e., the frequency itself, incorporates information as regards physical parameters of the system, i.e., f Elg , which is based on stiffness and mass of a physical system.
  • the at least one sensor component may be configured to at least partially identify the at least one characteristic eigenfrequency of the at least one railway component, which can be particularly advantageous, as the system may comprise multiple eigenfrequencies, also at higher frequencies, wherein the more eigenfrequencies can be captured, the more knowledge on the physical state of the system can be generated.
  • the system may be configured to estimate a value of at least one physical property of the at least one railway component, which is particularly advantageous as physical properties of railway components provide highest values for a latter health status monitoring.
  • the at least one analyzing component may be configured to at least partially identify the at least one characteristic eigenfrequency of the at least one railway component.
  • the at least one analyzing component may be configured to at least partially identify the at least one characteristic eigenfrequency of an interaction between at least 2 railway components of the railway network.
  • eigenfrequencies may be related to a railway component but also an interaction between 2 or more railway components of the railway network.
  • eigenfrequencies frequency and magnitude of oscillation
  • the at least one analyzing component may be configured to monitor at least one railway health status of at least one component of the railway network.
  • the at least one analyzing component may be configured to forecast at least one railway health status of at least one component of the railway network.
  • the at least one analyzing component may be configured to generate at least one railway health status hypothesis comprising at least one cause for the at least one railway health status of the at least one component of the railway network.
  • the at least one sensor data relevant to the railway network may comprise at least one railway infrastructural feature.
  • the at least one analyzing component may comprise a self-learning module.
  • the self-learning module may be configured to analyze the at least one sensor data.
  • the self-learning module may be configured to analyze at least one of: the at least one characteristic eigenfrequency, and the at least one physical parameter.
  • the self-learning module may be configured to determine changes over time of at least one of: the at least one sensor data, the at least one characteristic eigenfrequency, and the at least one physical parameter.
  • the self-learning module may be configured to correlate changes over time with at least one railway health status hypothesis.
  • the self-learning module may further be configured to execute at least one simulation model.
  • the system may comprise at least one computing component.
  • the at least one computing component is a remote computing component.
  • the at least one computing component is a local computing component.
  • the at least one computing component is integrated in the at least one sensor component.
  • the at least one computing component may be configured to pre-process the at least one sensor data.
  • the at least one computing component may comprise the self-learning module.
  • the at least one analyzing component may be configured to execute at least one analytical approach.
  • the system may further comprise at least one server.
  • the at least one server may comprise a remote server.
  • the at least one server may comprise a local server.
  • the at least one server may be configured to receive at least one sensor data relevant to the railway network.
  • the at least one server may be configured to monitor at least one of the at least one sensor data, the at least one characteristic eigenfrequency, and the at least one physical parameter.
  • the at least one server may be configured to provide at least one signal.
  • the at least one signal comprises the at least one sensor data. In another embodiment, the at least one signal comprises the at least one characteristic eigenfrequency. In a further embodiment, the at least one signal comprises the at least one physical property.
  • the at least one server may be configured to provide at least one signal comprising at least one railway infrastructural data.
  • the at least one signal is processed based on at least one analytical approach.
  • the at least one sensor may be configured to operate in a plurality of operation modes, and wherein each operation mode may be configured to monitor at least one sensor data relevant to railway network.
  • the at least one server may comprise an interface module configured to bidirectionally communicate with at least one authorized user.
  • the interface module may be configured to prompt at least one action by the at least one authorized user.
  • the at least one server may be configured to forecast health status of the at least one railway network based on at least one of: the at least one eigenfrequency, the at least one physical property, e.g., of the railway switch, and the at least one sensor data.
  • the at least one storing component may be configured to store data generated by the at least one server.
  • the at least one railway component may comprise at least one unmovable component, such as railway tracks.
  • the at least one railway component may comprise at least one movable component such as railway switches, frogs, rail barriers.
  • the processing component may be configured to generate structured datasets using the at least one sensor data.
  • the processing component may be configured to execute at least one analytical approach.
  • the analyzing component may be configured to execute at least one analytical approach.
  • the at least one server may comprise at least one of: the at least one processing component, and the at least one analyzing component.
  • the system may be configured to execute at least one machine learning algorithm.
  • the at least one machine learning may comprise pattern recognition.
  • the at least one processing component may be configured to (automatically) perform signal processing.
  • the at least one sensor may comprise an acceleration sensor. In another embodiment, the at least one sensor may comprise a velocity sensor. In an alternative embodiment, the at least one sensor may comprise a displacement sensor.
  • the at least one sensor may comprise a force sensor.
  • the at least one sensor may comprise a pressure sensor.
  • the force sensor is arranged between a ballast and a sleeper.
  • the force sensor is arranged between the sleeper and a rail.
  • the pressure sensor is arranged between the ballast and the sleeper.
  • the pressure sensor is arranged between the sleeper and the rail.
  • the at least one sensor may be configured to measure data in a vertical direction.
  • the at least one sensor may be configured to measure data in a longitudinal direction.
  • the at least one sensor may be configured to measure data in a lateral direction.
  • the invention in a second aspect, relates to a method for monitoring physical properties of at least one railway component of a railway network, the method comprising: collecting at least one sensor data at least one time via a least one sensor arranged on at least one railway component at least one excitation execution position, and determining at least one characteristic eigenfrequency of the at least one railway component.
  • This is particularly advantageous, as if multiple excitation execution positions for the force excitation are used different sub-systems or sub-components have more or less influence on the different measurements.
  • model calibration such as, for example, when the sleeper is provided with an excitation force and the sensor is mounted on the sleeper, then the rail and its mounting clamps have less influence on this measurement.
  • the rail and its mounting clamps have more influence on this measurement.
  • the method may further comprise estimating a value of at least one physical property of the at least one railway component.
  • the method may comprise processing the at least one sensor data via at least one processing component to generate at least one processed sensor data.
  • estimating the value of the at least one physical property of railway component is based on the at least one processed sensor data.
  • the method may comprise generating at least one railway component health status hypothesis of at least one component of the railway network.
  • generating at least one railway component health status hypothesis of at least one component of the railway network is based on the at the at least one processed sensor data.
  • the method may comprise forecasting at least one railway health status of at least one component of the railway network.
  • the method may comprise forecasting at least one railway health status of at least one component of the railway network based on the at least one railway component health hypothesis.
  • the method may comprise applying an excitation force to at least one railway component of the railway network, exciting at least one dynamic response to the excitation force of the at least one railway component of a railway network, measuring the at least one dynamic response to the excitation force of the at least one railway component of the railway network, and identifying a characteristic eigenfrequency of the at least one railway component of the railway network.
  • the characteristic eigenfrequency of the at least one railway component of the railway network is comprised by at least one of: the at least one sensor data, and the at least one processed sensor data.
  • the method may comprise correlating the at least one characteristic eigenfrequency with at least one railway component feature of the at least one railway component of the railway network.
  • the at least one railway component feature may comprise at least one geometrical feature.
  • the at least one railway component may comprise stiffness properties.
  • the at least one railway component may comprise damping properties.
  • the method according to any of the preceding method embodiments, wherein may comprise measuring the at least one dynamic response to the excitation force by means of the at least one sensor component.
  • the value of the at least one physical property of at least one railway component of the railway network may comprise at least one of: rail pad damping, rail pad stiffness, rail clamp damping, rail clamp stiffness, rail internal damping, rail shearing module, rail shearing coefficient, rail Young's modulus, rail density, rail shear tension, sleeper internal damping, sleeper shearing module, sleeper shearing coefficient, sleeper Young's modulus, sleeper density, sleeper shear tension, trackbed damping, trackbed stiffness, ballast shearing module, ballast shearing coefficient, ballast density, ballast cross-sectional area, ballast area moment of inertia.
  • the method may further comprise connecting the at least one sensor component to at least one server.
  • the method may comprise retrieving at least one signal comprising at least one of: the at least one sensor data, and the at least one processed sensor data.
  • the method may further comprise extracting at least one frequency information dataset from the at least one signal.
  • the at least one frequency information dataset may comprise at least one Power Spectral Density (PSD) dataset.
  • PSD Power Spectral Density
  • the at least one frequency information dataset may comprise at least one Fast Fourier Transform (FFT) dataset.
  • FFT Fast Fourier Transform
  • the at least one signal returns a PSD as at least one-dimensional vector of a fixed size.
  • the at least one signal may comprise a signal frequency content and a sampling rate, wherein the signal frequency is between 0 and 10,000 Hz, preferably between 50 and 8,000 Hz, more preferably between 50 and 5,000 Hz, and wherein the sampling rate is between 0 and 20 kHz, preferably between 0.1 and 8 kHz, more preferably between 1 and 5 kHz.
  • the method may comprise extracting at least one Mel spectrogram from the at least one signal.
  • the method may comprise mapping the data to generate a mapped dataset.
  • the method may comprise curating the estimating of the value of the at least one physical property of at least one railway component based on the mapped data.
  • the method may comprise unidirectionally connecting the at least one sensor component to the at least one server.
  • the method according to any of the embodiments Ml to M24, wherein the method may comprise bidirectionally connecting the at least one sensor component to the at least one server.
  • the method may comprise connecting the at least one server with the at least one processing component.
  • At least one of the at least one server may comprise at least partially one of the at least one processing component.
  • the method may comprise the at least one sensor component to supplying the at least one sensor data to the at least one sensor processing component.
  • the at least one processing component may comprise a storing component, wherein the method comprising storing at least one of: the at least one sensor data, and the at least one processed sensor data.
  • the sensor processing component may comprise a sensor storing component configured to store at least one of: the at least one sensor data, and the at least one processed sensor data.
  • the at least one signal may comprise at least one of: displacement data, velocity data, and acceleration data.
  • the method may comprise automatically transmitting to the at least one processing component the at least one sensor data.
  • the method may comprise pre-processing the at least one sensor data via the at least one processing component.
  • the method may further comprise supplying the at least one processing component with at least one neural network component.
  • the method may comprise feeding into the at least one neural network component the at least one sensor data.
  • the method may comprise feeding into the at least one neural network component the at least one pre-processed sensor data.
  • the at least one neural network may comprise at least one convolutional neural network layer.
  • the method may comprise using the outcome of the pre-processing for predicting at least one trend of the at least one physical property of the at least one railway component.
  • the method may comprise teaching the at least one neural network the at least one physical property of at least one railway component using at least one training database.
  • the at least one railway component may comprise trackbed.
  • the at least one railway component may comprise at least one railpad.
  • the at least one railway component may comprise at least one sleeper.
  • the at least one railway component may comprise at least one rail.
  • the method may further comprise generating at least one suggestion procedure to be applied on the at least one railway component.
  • the method may comprise generating at least one action instruction to be applied on the at least one railway component.
  • the method may further comprise prompting a user to implement at least one of: the at least one suggestion procedure, and the at least one action instruction.
  • the at least one sensor data may comprise a first sensor data and second sensor data.
  • the first sensor data is different from the second sensor data.
  • the at least one time may comprise a first time and a second time.
  • the first time is different from the second time.
  • the at least one excitation execution position may comprise a first excitation execution position and a second excitation execution position.
  • the first excitation execution position is different from the second excitation execution position.
  • the method may comprise: estimating a specific physical property of the at least one railway component to generate a first physical property finding, correlating the first physical property finding to at least health status, and generating at least one final physical property finding comprising the health status of the at least one railway component.
  • the at least one suggestion procedure and/or the at least one action instruction is based on the at least one final physical property finding.
  • the method may comprise of generating at least one causal hypothesis upon outputting the first physical property finding comprising a first health status of the at least one railway component of a railway network, outputting a second physical property finding comprising a second health status of the at least one railway component of a railway network, contrasting the first physical property finding with the second physical property finding, and predicting at least one causal event for a difference between the first physical property finding and the second physical property finding.
  • the first physical property finding and the second physical property finding relate to the same at least one railway component at least 2 different times.
  • the method may comprise using at least one supervised learning method.
  • the method may comprise using at least one unsupervised learning method.
  • the method may comprise automatically generating at least one interpreted dataset.
  • Automatically generating the at least one interpreted dataset may be performed in the at least one server.
  • the at least one geometrical feature may comprise a geometrical feature of at least one sleeper comprising at least one of: length, cross-sectional area, area moment of inertia, width, and height.
  • the at least one geometrical feature may comprise a geometrical feature of at least one sleeper comprising a distance between at least 2 sleepers.
  • the at least one geometrical feature may comprise a geometrical feature of at least one rail comprising at least one of: cross-sectional area of rail, area moment of inertia of rail, width of rail, height of rail
  • the at least one geometrical feature may comprise a geometrical feature of at least one sleeper comprising distance between at least 2 rails of a track.
  • the method may comprise identifying at least one of: a presence of a diverging track, mounting position of the sensor at the railway component, void under a sleeper, and presence of a movable or stiff frog.
  • the invention relates to use of the system as recited for carrying out the method as recited herein.
  • the invention relates to the use of the method as recited herein and the system as recited herein for analyzing physical properties of at least one railway component. Furthermore, the invention relates to a computer-implemented program comprising instructions which, when executed by a user-device, causes the user-device to carry out the method as recited herein.
  • the invention relates to a computer-implemented program comprising instructions which, when executed by a server, causes the at least one server to carry out the method as recited herein.
  • the invention relates to a computer-implemented program comprising instructions which, when executed causes by a user-device, causes the user-device and a server to carry out the method as recited herein.
  • the method may further comprise evaluating at least one of a simulation model and a model calibration procedure.
  • estimating the at least one physical property of the railway network may be based on evaluating step.
  • the method may further comprise automatically generating at least one simulated sensor data.
  • the method may comprise calculating characteristics of the at least one simulated sensor data.
  • the model calibration procedure comprises an optimization routine.
  • the method comprises at least one analytical approach.
  • the at least one analytical approach may comprise at least one of: signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • signal filter processing pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • ICA independent component analysis
  • the at least one analytical approach may be suitable for the model calibration procedure, the at least one analytical approach comprising at least one of: local optimization such as variations of gradient descent, Newton's method, Broyden-Fletcher- Goldfarb-Shanno algorithm, Nelder-Mead and other simplex based algorithms, Gauss- Newton, or Levenberg-Marquardt algorithm; global optimization such as evolutionary algorithms like genetic algorithm, simulated annealing, swarm intelligence, Sequential Model-based Global Optimization, Gaussian Process Approaches; machine learning optimization through reinforcement learning or pattern matching of inverse problem; and random search.
  • local optimization such as variations of gradient descent, Newton's method, Broyden-Fletcher- Goldfarb-Shanno algorithm, Nelder-Mead and other simplex based algorithms, Gauss- Newton, or Levenberg-Marquardt algorithm
  • global optimization such as evolutionary algorithms like genetic algorithm, simulated annealing, swarm intelligence, Sequential Model-based Global Optimization, Gaussian Process Approaches
  • a system for monitoring physical properties of at least one railway component of a railway network comprising at least one sensor component configured to sample at least one sensor data relevant to the railway network, at least one processing component configured to process the at least one sensor data, at least one storing component configured to store the at least one sensor data, and at least one analyzing component.
  • the at least one analyzing component is configured to receive the at least one sensor data from the at least one sensor component.
  • excitation force comprises a white noise.
  • excitation force comprises a sinusoidal sweep.
  • the excitation force comprises a frequency range between 0 and 10,000 Hz, preferably between 0 and 8,000 Hz, more preferably between 0 and 2,000 Hz.
  • the at least one sensor component is configured to measure at least one dynamic response to the excitation force of the at least one railway component of the railway network.
  • system is further configured to identify at least one characteristic eigenfrequency of the at least one railway component.
  • the at least one sensor component is configured to at least partially identify the at least one characteristic eigenfrequency of the at least one railway component.
  • the at least one analyzing component is configured to at least partially identify the at least one characteristic eigenfrequency of the at least one railway component.
  • the at least one analyzing component is configured to at least partially identify the at least one characteristic eigenfrequency of an interaction between at least 2 railway components of the railway network.
  • the at least one analyzing component is configured to generate at least one railway health status hypothesis comprising at least one cause for the at least one railway health status of the at least one component of the railway network.
  • the at least one analyzing component comprises a self-learning module.
  • the self learning module is configured to determine changes over time of at least one of the at least one sensor data, the at least one characteristic eigenfrequency, and the at least one physical parameter.
  • self-learning module is further configured to execute at least one simulation model.
  • system further comprises at least one server.
  • the at least one server comprises an interface module configured to bidirectionally communicate with at least one authorized user.
  • the at least one server is configured to forecast health status of the at least one railway network based on at least one of the at least one eigenfrequency, the at least one physical property and the at least one sensor data.
  • the at least one storing component is configured to store data generated by the at least one server.
  • the at least one railway component comprises at least one unmovable component, such as railway tracks.
  • the at least one railway component comprises at least one movable component such as railway switches, frogs, rail barriers.
  • processing component is configured to generate structured datasets using the at least one sensor data.
  • processing component is configured to execute at least one analytical approach.
  • At least one server comprises at least one of the at least one processing component, and the at least one analyzing component.
  • the at least one sensor comprises a velocity sensor. 560. The system according to any of the preceding embodiments wherein the at least one sensor comprises a displacement sensor.
  • a method for monitoring physical properties of at least one railway component of a railway network comprising collecting at least one sensor data at least one time via a least one sensor arranged on at least one railway component at least one excitation execution position, and determining at least one characteristic eigenfrequency of the at least one railway component.
  • the method comprises applying an excitation force to at least one railway component of the railway network, exciting at least one dynamic response to the excitation force of the at least one railway component of a railway network, measuring the at least one dynamic response to the excitation force of the at least one railway component of the railway network, and identifying a characteristic eigenfrequency of the at least one railway component of the railway network.
  • the value of the at least one physical property of at least one railway component of the railway network comprises at least one of: rail pad damping, rail pad stiffness, rail clamp damping, rail clamp stiffness, rail internal damping, rail shearing module, rail shearing coefficient, rail Young's modulus, rail density, rail shear tension, sleeper internal damping, sleeper shearing module, sleeper shearing coefficient, sleeper Young's modulus, sleeper density, sleeper shear tension, trackbed damping, trackbed stiffness, ballast shearing module, ballast shearing coefficient, ballast density, ballast cross-sectional area, ballast area moment of inertia. M17.
  • the method further comprises connecting the at least one sensor component to at least one server.
  • the at least one frequency information dataset comprises at least one Power Spectral Density (PSD) dataset.
  • PSD Power Spectral Density
  • the at least one frequency information dataset comprises at least one Fast Fourier Transform (FFT) dataset.
  • FFT Fast Fourier Transform
  • the at least one signal comprises a signal frequency content and a sampling rate, wherein the signal frequency is between 0 and 10,000 Hz, preferably between 50 and 8,000 Hz, more preferably between 50 and 5,000 Hz, and wherein the sampling rate is between 0 and 20 kHz, preferably between 0.1 and 8 kHz, more preferably between 1 and 5 kHz.
  • M26 The method according to the preceding embodiment, wherein the method comprises curating the estimating of the value of the at least one physical property of at least one railway component based on the mapped data.
  • M27 The method according to any of the preceding method embodiments, wherein the method comprises unidirectionally connecting the at least one sensor component to the at least one server.
  • the at least one processing component comprises a storing component, wherein the method comprising storing at least one of the at least one sensor data, and the at least one processed sensor data.
  • the sensor processing component comprises a sensor storing component configured to store at least one of the at least one sensor data, and the at least one processed sensor data.
  • the at least one sensor data comprises a first sensor data and second sensor data.
  • the at least one excitation execution position comprises a first excitation execution position and a second excitation execution position.
  • M55 The method according to any of the preceding method embodiments, wherein the first excitation execution position is different from the second excitation execution position.
  • M56 The method according to any of the preceding method embodiments and with features of embodiments M5, wherein the method comprises estimating a specific physical property of the at least one railway component to generate a first physical property finding, correlating the first physical property finding to at least health status, and generating at least one final physical property finding comprising the health status of the at least one railway component.
  • M62 The method according to any of the preceding method embodiments, wherein the method comprises using at least one unsupervised learning method.
  • M63. The method according to any of the preceding method embodiments, wherein the method comprises automatically generating at least one interpreted dataset.
  • the at least one geometrical feature comprises a geometrical feature of at least one sleeper comprising at least one of length, cross-sectional area, area moment of inertia, width, and height.
  • the at least one geometrical feature comprises a geometrical feature of at least one rail comprising at least one of cross-sectional area of rail area moment of inertia of rail width of rail height of rail
  • M69 The method according to any of the preceding method, wherein the method comprises identifying at least one of a presence of a diverging track, mounting position of the sensor at the railway component, void under a sleeper, and presence of a movable or stiff frog.
  • M70 The method according to any of the preceding method embodiments, wherein the method further comprises evaluating at least one of a simulation model, and a model calibration procedure.
  • the at least one analytical approach comprises at least one of: signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • the at least one analytical approach comprises at least one of: signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • ICA independent component analysis
  • the at least one analytical approach is suitable the model calibration procedure, the at least one analytical approach comprising at least one of: local optimization such as variations of gradient descent, Newton's method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Nelder-Mead and other simplex based algorithms, Gauss-Newton, or Levenberg-Marquardt algorithm; global optimization such as evolutionary algorithms like genetic algorithm, simulated annealing, swarm intelligence, Sequential Model-based Global Optimization, Gaussian Process Approaches; machine learning optimization through reinforcement learning or pattern matching of inverse problem; and random search.
  • local optimization such as variations of gradient descent, Newton's method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Nelder-Mead and other simplex based algorithms, Gauss-Newton, or Levenberg-Marquardt algorithm
  • global optimization such as evolutionary algorithms like genetic algorithm, simulated annealing, swarm intelligence, Sequential Model-based Global Optimization, Gaussian Process Approaches
  • program embodiments will be discussed. These embodiments are abbreviated by the letter “C” followed by a number. Whenever reference is herein made to “program embodiments”, these embodiments are meant.
  • a computer-implemented program comprising instructions which, when executed by a user-device, causes the user-device to carry out the method steps according to any of the preceding method embodiments.
  • a computer-implemented program comprising instructions which, when executed by a server, causes the at least one server to carry out the method steps according to any of the preceding method embodiments.
  • a computer-implemented program comprising instructions which, when executed causes by a user-device, causes the user-device and a server to carry out the method steps according to any of the preceding method embodiments.
  • Fig. 1 depicts a schematic representation of a railway network and system arranged at the railway network
  • Fig. 2 depicts a system for monitoring a railway network according to embodiments of the present invention
  • Fig. 3 depicts a schematic of a computing device
  • Fig. 4 depicts a schematic of the method according to embodiments of the present invention.
  • Fig. 1 depicts a schematic representation of a railway network and system arranged at the railway network.
  • the system may comprise a railway section with the railway 1 itself, comprising rails 10 and sleepers 3. Instead of the sleepers 3 also a solid bed for the rails 10 can be provided.
  • a further example of constitutional elements is conceptually represented a mast, conceptually identified by reference numeral 6. Such constitutional elements are usually arranged at or in the vicinity of railways. Furthermore, a tunnel is shown, conceptually identified by reference numeral 5. It should be understood that other constructions, buildings etc. may be present and also used for the present invention as described before and below.
  • a first sensor 2 can be arranged on one or more of the sleepers.
  • the sensor 2 can be an acceleration sensor and/or any other kind of railway specific sensor. Examples have been mentioned before.
  • a second sensor 9 can also arranged on another sleeper distant from the first sensor 2. Although it seems just a small distance in the present example, those distances can range from the distance to the neighboring sleeper to one or more kilometers. Other sensors can be used for attachment to the sleepers as well. The sensors can further be of different kind - such as where the first sensor 2 may be an acceleration sensor, the second sensor 9 can be a magnetic sensor or any other combination suitable for the specific need. The variety of sensors are enumerated before.
  • any of the sensors for example, the first sensor 2 and/or the second sensor 9, can directly be attached to the rail.
  • the sensors for example the first sensor 2 and/or the second sensor 9, further comprise a wireless sensor network.
  • the sensor node can transmit data to a base station (not shown here).
  • the base station can be installed to the railway component.
  • the base station can also be installed in the surroundings of the railway component.
  • the base station can also be a remote base station.
  • the communication module between the base station and the sensor node (s) can comprise, for example Xbee with a frequency of 868 MHz, but is not limited to this.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can also be installed in cases and inserted inside the railway component, for example inside a special hole carved into the concrete.
  • the case can also be attached to the railway component using fixers.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can be obtaining at least one sensor data based on acceleration, inclination, distance, etc.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, may further be divided into group, for example based on the distance.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9 lying within a pre-determined distance may be controlled by one base station.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can also be installed on the moving railway component such as on-board of a vehicle.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise an amplifier to amplify any signal received by the base station.
  • the sensor node for example the first sensor 2 and/or the second sensor 9, can be installed such that the sensor node lying within one group can communicate with their base station in one-hop.
  • the base station can receive information from its 'neighbors' and retransmit all the information to the server 800.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise sensor(s).
  • the sensor can be accelerometers, such as Sensor4PRI for example ADCL 345, SQ-SVS etc.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise inclinometers, such as SQ-SI-360DA, SCA100T-D2, ADXL345 etc.
  • the sensor node can further comprise distance sensors.
  • the distance sensors can be configured to at least measure the distance between slab tracks, using infrared and/or ultrasonic.
  • the distance sensor can be for example, MB1043, SRF08, PING, etc.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise visual sensors, such as 3D cameras, speed enforcement cameras, traffic enforcement cameras, etc. It may be noted that sensor node(s) may comprise sensors to observe the physical environment of the infrastructure the sensor node(s) are installed in. For example, but not limited to, temperature sensor, humidity sensor, altitude sensor, pressure sensor, GPS sensor, water pressure sensor, piezometer, multi-depth deflectometers (MDD), acceleration.
  • MMDD multi-depth deflectometers
  • the sensor node for example the first sensor 2 and/or the second sensor 9, can be installed according to a protocol based on routing trees to be able to transmit information to the base station. Once the information has been received, a cellular network can be used to send at least one sensor data to a remote server 800.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise an analog-to-digital converter, a micro controller, a transceiver, power and memory.
  • One or more sensor(s) can be embedded in different elements and can be mounted on boards to be attached to the railway component.
  • the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can also comprise materializing strain gauges, displacement transducers, accelerometers, inclinometers, acoustic emission, thermal detectors, among others.
  • the analog signal outputs generated by the sensors can be converted to digital signals that can be processed by digital electronics.
  • the data can then be transmitted to the base station by a microcontroller through a radio transceiver. All devices can be electric or electronic components supported by power supply, which can be provided through batteries or by local energy generation (such as solar panels), the latter mandatory at locations far away from energy supplies.
  • the at least one sensor data collected from the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can be transferred to the base station using wireless communication technology such as Wi-Fi, -Bluetooth, ZigBee or any other proprietary radio technologies suitable for the purpose.
  • wireless communication technology such as Wi-Fi, -Bluetooth, ZigBee or any other proprietary radio technologies suitable for the purpose.
  • the ZigBee network can be advantageous to consumes less power.
  • long-range communication such a cellular network or satellite can be used as well as wired technologies based on optical fiber.
  • This may be a different kind of sensor, such as, for example, an optical, temperature, even acceleration sensor, etc.
  • a further kind of sensor, for example sensor 8 can be arranged above the railway as at the beginning or within the tunnel 5. This could, for example, be a height sensor for determining the height of a train, an optical sensor, a doppler sensor etc. It will be understood that all those sensors mentioned here and/or before are just non-limiting examples.
  • the sensors can be configured to submit the at least one sensor data via a communication network, such as a wireless communication network.
  • a communication network such as a wireless communication network.
  • the communication network bears several advantages and disadvantages regarding availability, transmittal distance, costs etc. the transmittal of at least one sensor data is optimized as described herein before and below.
  • Fig. 2 depicts a system 100 monitoring a railway network.
  • the system 100 may comprise a sensor component 200, a processing component 300, a storing component 400, an analyzing component 500 and a server 600.
  • the sensor component 200 may comprise a plurality of sensor units, and each may comprise a plurality of sensor nodes. Therefore, the sensor component 200 may also be referred to as a plurality of sensor components 200. Additionally or alternatively, the sensor component may be configured to sample information relevant to a railway network, for instance, electric current based information of a given component and/part of a railway network.
  • the processing 300 component may comprise a standalone component configure to retrieve information from the sensor 200. Additionally or alternatively, the processing component may be configured to bidirectionally communicate the storing component 300 and the analyzing component 500. For instance, the processing component 300 may transfer raw at least one sensor data to the storing component 400, wherein the raw at least one sensor data may be stored until the processing component 300 may require said data for processing to generate a processed at least one sensor data. In another embodiment, the processing component 300 may also transfer processed at least one sensor data to the storing component 400. In a further embodiment, the processing component may also retrieve data from the storing component 400.
  • the analyzing component 500 may be configured to bidirectionally communicate with the processing component 300, the storing component 400 and/or the server 600. It will be understood that the communication of the analyzing component 500 with the other components may take place independent and/or simultaneously one from another.
  • the processing component 300 may also be integrated with at least one of the sensors 200.
  • the processing component 300 may also comprise an imbedded module of the sensors 200.
  • the analyzing component 500 may be configured to process at least one sensor data based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • ICA independent component analysis
  • the server 600 may comprise one or more modules configured to receive information from the analyzing component 500.
  • the sensor 200, the processing component 300, the storing component 400 and the analyzing component may comprise an integrated module configured to execute subsequently the tasks corresponding to each individual component, and transfer a final processed analyzed at least one sensor data to the server 600.
  • the sensor 200, the processing component 300, the storing component 400 and the analyzing component 500 may comprises modules of a single component.
  • the server 600 may retrieve information from the analyzing component 500, and further may provide information to the analyzing component 500, for example, operation parameters. It will be understood that each component may receive a plurality of operation parameters, for instance, the processing component 300 may be commanded to execute a preprocessing of the data received from the sensors 200.
  • the processing component 300 may be instructed to transmit the original data received from the sensors 200, i.e., the data coming from the sensors 200 can be transferred directly to the next component without executing any further task. It will be understood that the component may also be configured to perform a plurality of tasks at the same time, e.g., processing the data coming from the sensor 200 before transferring to the next component and transferring the data coming from the sensors 200 without any processing.
  • the server 600 may comprise a cloud server, a remote server and/or a collection of different type of servers. Therefore, the server 600 may also be referred to as cloud server 600, remote server 600, or simple as servers 500. In another embodiment, the servers 500 may also converge in a central server.
  • each component may also comprise a remote communication unit configured to establish a remote communication between a component, e.g., sensor component 200, with the server 600.
  • the storing component 400 may be configured to receive information from the server 600 for storage.
  • the storing component 400 may store information provided by the servers 600.
  • the information provided by the server 600 may include, for example, but not limited to, data obtained by sensors 200, data processed by the processing component 500 and any additional data generated in the servers 600.
  • the servers 600 may be granted access to the storing component 400 comprising, inter alia, the following permissions, reading the data allocated in the storing component 400, writing and overwriting the data stored in the storing component 400, control and modify the storage logic and the data distribution within the storing component 400.
  • the server 600 may be configured transmit a signal to other component of the railway system based upon health status information retrieved from sensors 200. For instance, a giving health status data is provided by the server 600 and subsequently the server 600 generates a signal containing instructions, which are transmitted to the railway system for implementation.
  • the set of instructions may comprise, inter alia, generating a hypothesis as regards the health status of the railway network and/or a failure hypothesis, which may comprise instructions to be implemented before a failure occurs on the railway network, such as switching rolling unit from on track to another.
  • the signal may be based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • ICA independent component analysis
  • the sensors 200 may, inter alia, adopt a configuration that allows identifying trains, their speeds and their wear effect on the tracks.
  • the data gathered by the sensors 200 may constitute the basis for the server 600 to generate instructions for the activation of the switches.
  • the sensors 200 may retrieve data that may allow activating the switches in order to redirect the trains, for example, from track 1 to track 2, according to their speed and/or wear effect.
  • the data gathered by the sensors 200 may be communicated to the server 600, which may subsequently transmit the information and the corresponding instructions to the nearest assets, for example, the nearest switch, which may consequently be activated to control the traffic on the tracks.
  • the system 100 may estimate the health status of components of the railway network and may further generate a health status and/or failure hypothesis that may allow to forecast the suitability of the component of the railway network to allocate rolling units.
  • a health status and/or failure hypothesis may be based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • ICA independent component analysis
  • the system 100 may determine that a particular part and/or component of the railway network, for instance, a given section of track and/or a switch, is required to be replaced and/or maintain before a given date to avoid failure of the railway.
  • the system 100 may also determine that a particular rolling stock may pass through a component or portion of the railway network requiring maintenance, reparation or replacement, however, due to work schedule it may be prompt to failure if an inadequate rolling unit passes through.
  • This approach may be advantageous, as it may allow to reduce failure of railway networks, which may be achieved by monitoring, evaluating and forecasting optimal operation conditions of the railway network.
  • system 100 may be configured to predict a future status of the railway network and based on that may determine an optimal operation conditions using data analysis based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • ICA independent component analysis
  • determinations of the system 100 may directly be used forecast xxx failure, which may be advantageous for planning and execution of maintenance and/or inspections of railway network, which may further allow to minimize downtime of single machines and more importantly an adjacent railway network.
  • Such monitoring, analyzing and forecasting may be based on machine learning comprising predicting health status hypothesis and/or failure hypothesis based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
  • ICA independent component analysis
  • Fig. 3 depicts a schematic of a computing device 1000.
  • the computing device 1000 may comprise a computing unit 35, a first data storage unit 30A, a second data storage unit 30B and a third data storage unit 30C.
  • the computing device 1000 can be a single computing device or an assembly of computing devices.
  • the computing device 1000 can be locally arranged or remotely, such as a cloud solution.
  • the different data can be stored, such as the genetic data on the first data storage 30A, the time stamped data and/or event code data and/or phenotypic data on the second data storage 30B and privacy sensitive data, such as the connection of the before-mentioned data to an individual, on the thirds data storage 30C.
  • Additional data storage can be also provided and/or the ones mentioned before can be combined at least in part.
  • Another data storage (not shown) can comprise data specifying for instance, air temperature, rail temperature, position of blades, model of xxx, position of xxx and/or further railway network related information. This data can also be provided on one or more of the before-mentioned data storages.
  • the computing unit 35 can access the first data storage unit 30A, the second data storage unit 30B and the third data storage unit 30C through the internal communication channel 160, which can comprise a bus connection 160.
  • the computing unit 30 may be single processor or a plurality of processors, and may be, but not limited to, a CPU (central processing unit), GPU (graphical processing unit), DSP (digital signal processor), APU (accelerator processing unit), ASIC (application-specific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (field programable gate array).
  • the first data storage unit 30A may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).
  • RAM random-access memory
  • DRAM Dynamic RAM
  • SDRAM Synchronous Dynamic RAM
  • SRAM static RAM
  • Flash Memory Magneto-resistive RAM
  • MRAM Magneto-resistive RAM
  • F-RAM Ferroelectric RAM
  • the second data storage unit 30B may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).
  • RAM random-access memory
  • DRAM Dynamic RAM
  • SDRAM Synchronous Dynamic RAM
  • SRAM static RAM
  • Flash Memory Flash Memory
  • MRAM Magneto-resistive RAM
  • F-RAM Ferroelectric RAM
  • P-RAM Parameter RAM
  • the third data storage unit 30C may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).
  • RAM random-access memory
  • DRAM Dynamic RAM
  • SDRAM Synchronous Dynamic RAM
  • SRAM static RAM
  • Flash Memory Flash Memory
  • Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM Parameter RAM
  • the first data storage unit 30A (also referred to as encryption key storage unit 30A), the second data storage unit 30B (also referred to as data share storage unit 30B), and the third data storage unit 30C (also referred to as decryption key storage unit 30C) can also be part of the same memory.
  • only one general data storage unit 30 per device may be provided, which may be configured to store the respective encryption key (such that the section of the data storage unit 30 storing the encryption key may be the encryption key storage unit 30A), the respective data element share (such that the section of the data storage unit 30 storing the data element share may be the data share storage unit 30B), and the respective decryption key (such that the section of the data storage unit 30 storing the decryption key may be the decryption key storage unit 30A).
  • the respective encryption key such that the section of the data storage unit 30 storing the encryption key may be the encryption key storage unit 30A
  • the respective data element share such that the section of the data storage unit 30 storing the data element share may be the data share storage unit 30B
  • the respective decryption key such that the section of the data storage unit 30 storing the decryption key may be the decryption key storage unit 30A).
  • the third data storage unit 30C can be a secure memory device 30C, such as, a self-encrypted memory, hardware-based full disk encryption memory and the like which can automatically encrypt all of the stored data.
  • the data can be decrypted from the storing component only upon successful authentication of the party requiring to access the third data storage unit 30C, wherein the party can be a user, computing device, processing unit and the like.
  • the third data storage unit 30C can only be connected to the computing unit 35 and the computing unit 35 can be configured to never output the data received from the third data storage unit 30C. This can ensure a secure storing and handling of the encryption key (i.e., private key) stored in the third data storage unit 30C.
  • the second data storage unit 30B may not be provided but instead the computing device 1000 can be configured to receive a corresponding encrypted share from the database 60.
  • the computing device 1000 may comprise the second data storage unit 30B and can be configured to receive a corresponding encrypted share from the database 60.
  • the computing device 1000 may comprise a further storing component 140 which may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).
  • the storing component 140 may also be connected with the other components of the computing device 1000 (such as the computing component 35) through the internal communication channel 160.
  • the computing device 1000 may comprise an external communication component 130.
  • the external communication component 130 can be configured to facilitate sending and/or receiving data to/from an external device (e.g., backup device 10, recovery device 20, database 60).
  • the external communication component 130 may comprise an antenna (e.g., WIFI antenna, NFC antenna, 2G/3G/4G/5G antenna and the like), USB port/plug, LAN port/plug, contact pads offering electrical connectivity and the like.
  • the external communication component 130 can send and/or receive data based on a communication protocol which can comprise instructions for sending and/or receiving data. Said instructions can be stored in the storing component 140 and can be executed by the computing unit 35 and/or external communication component 130.
  • the external communication component 130 can be connected to the internal communication component 160.
  • data received by the external communication component 130 can be provided to the storing component 140, computing unit 35, first data storage unit 30A and/or second data storage unit 30B and/or third data storage unit 30C.
  • data stored on the storing component 140, first data storage unit 30A and/or second data storage unit 30B and/or third data storage unit 30C and/or data generated by the commuting unit 35 can be provided to the external communication component 130 for being transmitted to an external device.
  • the computing device 1000 may comprise an input user interface 110 which can allow the user of the computing device 1000 to provide at least one input (e.g., instruction) to the computing device 100.
  • the input user interface 110 may comprise a button, keyboard, trackpad, mouse, touchscreen, joystick and the like.
  • the computing device 1000 may comprise an output user interface 120 which can allow the computing device 1000 to provide indications to the user.
  • the output user interface 110 may be a LED, a display, a speaker and the like.
  • the output and the input user interface 100 may also be connected through the internal communication component 160 with the internal component of the device 100.
  • the processor may be singular or plural, and may be, but not limited to, a CPU, GPU, DSP, APU, or FPGA.
  • the memory may be singular or plural, and may be, but not limited to, being volatile or non-volatile, such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM.
  • the data processing device can comprise means of data processing, such as, processor units, hardware accelerators and/or microcontrollers.
  • the data processing device 20 can comprise storing components, such as, main memory (e.g., RAM), cache memory (e.g., SRAM) and/or secondary memory (e.g., HDD, SDD).
  • the data processing device can comprise busses configured to facilitate data exchange between components of the data processing device, such as, the communication between the storing components and the processing components.
  • the data processing device can comprise network interface cards that can be configured to connect the data processing device to a network, such as, to the Internet.
  • the data processing device can comprise user interfaces, such as: output user interface, such as: o screens or monitors configured to display visual data (e.g., displaying graphical user interfaces of railway network status), o speakers configured to communicate audio data (e.g., playing audio data to the user), input user interface, such as: o camera configured to capture visual data (e.g., capturing images and/or videos of the user), o microphone configured to capture audio data (e.g., recording audio from the user), o keyboard configured to allow the insertion of text and/or other keyboard commands (e.g., allowing the user to enter text data and/or other keyboard commands by having the user type on the keyboard) and/or trackpad, mouse, touchscreen, joystick - configured to facilitate the navigation through different graphical user interfaces of the questionnaire.
  • output user interface such as: o screens or monitors configured to display visual data (e.g., displaying graphical user interfaces of railway network status), o speakers configured to communicate audio data (e.g., playing audio data to the
  • the data processing device can be a processing unit configured to carry out instructions of a program.
  • the data processing device can be a system-on-chip comprising processing units, storing components and busses.
  • the data processing device can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer.
  • the data processing device can be a server, either local and/or remote.
  • the data processing device can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and/or user interface (such as the upper-mentioned user interfaces).
  • Fig. 4 depicts a schematic of the method according to embodiments of the present invention according to embodiments of the present invention. In simple terms, Fig.
  • the invention comprises using an excitation force 20 at least one time, for instance, at times tl, t2, and tx, wherein the excitation force 20 is applied to at least one railway component RC such as RC1, RC2 an RCx.
  • the railway component RC can be a railway switch 12 comprising the sensor 200 mounted on the railway switch 12.
  • the sensor 200 may measure sensor data which may be used to calculate characteristics of the sensor data.
  • the invention may further comprise a step 26 for identifying eigenfrequencies.
  • the invention may also comprise at least one of: a model calibration procedure 282, calculating step 284 for calculating characteristics of simulated sensor data and evaluation 286 of simulation railway components RC, such as the railway switch 12. Furthermore, the invention comprises identifying of health status of the railway component RC, such as the railway switch 12 by means of alternative a) and/or alternative b).
  • the term "at least one of a first option and a second option" is intended to mean the first option or the second option or the first option and the second option.
  • step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z).
  • step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z).

Abstract

The present invention relates to a system (100) for monitoring physical properties of at least one railway component of a railway network, the system comprising at least one sensor component (200) configured to sample at least one sensor data relevant to the railway network, at least one processing component (300) configured to process the at least one sensor data, at least one storing component (400) configured to store the sensor data, and at least one analyzing component (500). The present invention also relates to a method for monitoring physical properties of at least one railway component of a railway network, the method comprising collecting at least one sensor data at least one time via a least one sensor arranged on at least one railway component at least one excitation execution position, and determining at least one characteristic eigenfrequency of the at least one railway component.

Description

Railway monitoring system and method
Field
The invention lies in the field of railways monitoring and particularly in the field of analyzing physical properties of railway components. More particularly, the present invention relates to a system for analyzing physical properties of a railway network, a method performed in such a system and corresponding use of a system.
Background
Railroad, railway or rail transport has been developed for transferring goods and passengers on wheeled vehicles on rails, also known as tracks. In contrast to road transport, where vehicles run on a prepared flat surface, rail vehicles (rolling stock) are directionally guided by the tracks on which they run. Tracks commonly consist of steel rails, installed on ties or sleepers and ballast, on which the rolling stock, usually provided with metal wheels, moves. Other variations are also possible, such as slab track, where the rails are fastened to a concrete foundation resting on a subsurface.
Rolling stock in a rail transport system generally encounters lower frictional resistance than road vehicles, so passenger and freight cars (carriages and wagons) can be coupled into longer trains. Power is provided by locomotives, which either draw electric power from a railway electrification system or produce their own power, usually by diesel engines. Most tracks are accompanied by a signaling system. Railways are a safe land transport system when compared to other forms of transport. Additionally, railways are capable of high levels of passenger and cargo utilization and energy efficiency but are often less flexible and more capital-intensive than road transport, when lower traffic levels are considered.
The inspection of railway equipment is essential for the safe movement of trains. Many types of defect detectors are in use today. These devices utilize technologies that vary from a simplistic paddle and switch to infrared and laser scanning, and even ultrasonic audio analysis. Their use has avoided many rail accidents over the past decades.
Railway operations require careful monitoring and control of the conditions of the railway component to ensure passenger safety and reliable service. Many sensors are used to monitor and obtain data from different infrastructural components of the railway network, which may be used to ensure the integrity of the service and identify possible sources of malfunction. Such sensors allow for data collection and analysis and ensure safer operations of railways. Various sensors can be placed directly on trains, on tracks or nearby, at train stations and/or on platforms, and generally in the overall vicinity of the railway.
Measurements of such sensors may be used to further measurements, control, prediction and optimization of operation of railways.
Kaewunruen et al. (DOI: 10.1016/j.ndteint.2007.03.004) discloses an approach to judge the dynamic responses, for example, frequency response functions, together with a visual inspection of existing field conditions. In particular, Kaewunruen et a!. relates to examples of testing results representing a deficient integrity and based on such results.
Lam et al. (DOI: 10.1016/j.proeng.2011.07.022) reports a feasibility study in the use of measured modal parameters of in-situ concrete sleepers to detect possible damage of the underlying railway ballast, wherein ballast damage is defined as ballast degradation and ballast cementation with the accumulation of fines.
Oregui et al. (ISMA2010) relates to an approach for detection and maintenance of tracks against squats and corrugation. In particular, it relates to a method to relate track parameters to squats and corrugation initiation and growth, and to a finite element model.
Summary
In light of the above, it is an object of the present invention to overcome or at least alleviate the shortcomings of the prior art. More particularly, it is an object of the present invention to provide a system and a method for analyzing physical properties of railway components comprised by the railway network.
It is also a preferred object of the present invention monitoring health status of a railway network, in particular, components of a railway component comprised by the railway network.
These objects are met by the present invention.
In a first aspect, the invention relates to a system for monitoring physical properties of at least one railway component of a railway network, the system comprising: at least one sensor component configured to sample at least one sensor data relevant to the railway network, at least one processing component configured to process the at least one sensor data, at least one storing component configured to store the sensor, and at least one analyzing component. The at least one analyzing component may be configured to receive the at least one sensor data from the at least one sensor component.
The system may comprise an excitation component configured to provide an excitation force to at least one railway component of the railway network. This is particularly advantageous, as characteristics of the excitation force are known beforehand, which results in an improvement in respect with other approaches, such as, systems configured to work with a train-induced excitation.
In one embodiment, the excitation force may comprise an impulse force. In a further embodiment the excitation force may comprise a step force. In another embodiment, the excitation force may comprise a white noise. In a further embodiment, the excitation force may comprise a sinusoidal sweep. Additionally or alternatively, the excitation force may comprise a colored noise. The excitation force may comprise a frequency range between 0 and 10,000 Hz, preferably between 0 and 8,000 Hz, more preferably between 0 and 2,000 Hz.
The at least one sensor component may be configured to measure at least one dynamic response to the excitation force of the at least one railway component of the railway network.
The system may further be configured to identify at least one characteristic eigenfrequency of the at least one railway component. This is particularly advantageous, as a location of the at least one eigenfrequency, i.e., the frequency itself, incorporates information as regards physical parameters of the system, i.e., fElg , which is based on stiffness and mass of a physical system.
The at least one sensor component may be configured to at least partially identify the at least one characteristic eigenfrequency of the at least one railway component, which can be particularly advantageous, as the system may comprise multiple eigenfrequencies, also at higher frequencies, wherein the more eigenfrequencies can be captured, the more knowledge on the physical state of the system can be generated.
The system may be configured to estimate a value of at least one physical property of the at least one railway component, which is particularly advantageous as physical properties of railway components provide highest values for a latter health status monitoring. The at least one analyzing component may be configured to at least partially identify the at least one characteristic eigenfrequency of the at least one railway component.
The at least one analyzing component may be configured to at least partially identify the at least one characteristic eigenfrequency of an interaction between at least 2 railway components of the railway network. This is particularly beneficial, as eigenfrequencies may be related to a railway component but also an interaction between 2 or more railway components of the railway network. For instance, eigenfrequencies (frequency and magnitude of oscillation) can be defined by one railway component itself such as bending eigenfrequencies of a sleeper as it behaves as a beam, but they can also be defined by parameters of 2 railway components, such as the eigenfrequency of a movement of a rigid sleeper that is defined by a stiffness of a trackbed and a mass of the sleeper.
The at least one analyzing component may be configured to monitor at least one railway health status of at least one component of the railway network.
The at least one analyzing component may be configured to forecast at least one railway health status of at least one component of the railway network.
The at least one analyzing component may be configured to generate at least one railway health status hypothesis comprising at least one cause for the at least one railway health status of the at least one component of the railway network.
The at least one sensor data relevant to the railway network may comprise at least one railway infrastructural feature.
The at least one analyzing component may comprise a self-learning module.
The self-learning module may be configured to analyze the at least one sensor data.
The self-learning module may be configured to analyze at least one of: the at least one characteristic eigenfrequency, and the at least one physical parameter.
The self-learning module may be configured to determine changes over time of at least one of: the at least one sensor data, the at least one characteristic eigenfrequency, and the at least one physical parameter.
The self-learning module may be configured to correlate changes over time with at least one railway health status hypothesis. The self-learning module may further be configured to execute at least one simulation model.
The system may comprise at least one computing component.
The at least one computing component is a remote computing component.
The at least one computing component is a local computing component.
The at least one computing component is integrated in the at least one sensor component.
The at least one computing component may be configured to pre-process the at least one sensor data.
The at least one computing component may comprise the self-learning module.
The at least one analyzing component may be configured to execute at least one analytical approach.
The system may further comprise at least one server.
The at least one server may comprise a remote server.
The at least one server may comprise a local server.
The at least one server may be configured to receive at least one sensor data relevant to the railway network.
The at least one server may be configured to monitor at least one of the at least one sensor data, the at least one characteristic eigenfrequency, and the at least one physical parameter.
The at least one server may be configured to provide at least one signal.
In one embodiment, the at least one signal comprises the at least one sensor data. In another embodiment, the at least one signal comprises the at least one characteristic eigenfrequency. In a further embodiment, the at least one signal comprises the at least one physical property. The at least one server may be configured to provide at least one signal comprising at least one railway infrastructural data.
The at least one signal is processed based on at least one analytical approach.
The at least one sensor may be configured to operate in a plurality of operation modes, and wherein each operation mode may be configured to monitor at least one sensor data relevant to railway network.
The at least one server may comprise an interface module configured to bidirectionally communicate with at least one authorized user.
The interface module may be configured to prompt at least one action by the at least one authorized user.
The at least one server may be configured to forecast health status of the at least one railway network based on at least one of: the at least one eigenfrequency, the at least one physical property, e.g., of the railway switch, and the at least one sensor data.
The at least one storing component may be configured to store data generated by the at least one server.
The at least one railway component may comprise at least one unmovable component, such as railway tracks.
The at least one railway component may comprise at least one movable component such as railway switches, frogs, rail barriers.
The processing component may be configured to generate structured datasets using the at least one sensor data.
The processing component may be configured to execute at least one analytical approach.
The analyzing component may be configured to execute at least one analytical approach.
The at least one server may comprise at least one of: the at least one processing component, and the at least one analyzing component. The system may be configured to execute at least one machine learning algorithm.
The at least one machine learning may comprise pattern recognition.
The at least one processing component may be configured to (automatically) perform signal processing.
In one embodiment, the at least one sensor may comprise an acceleration sensor. In another embodiment, the at least one sensor may comprise a velocity sensor. In an alternative embodiment, the at least one sensor may comprise a displacement sensor.
The at least one sensor may comprise a force sensor.
The at least one sensor may comprise a pressure sensor.
The force sensor is arranged between a ballast and a sleeper.
The force sensor is arranged between the sleeper and a rail.
The pressure sensor is arranged between the ballast and the sleeper.
The pressure sensor is arranged between the sleeper and the rail.
The at least one sensor may be configured to measure data in a vertical direction.
The at least one sensor may be configured to measure data in a longitudinal direction.
The at least one sensor may be configured to measure data in a lateral direction.
In a second aspect, the invention relates to a method for monitoring physical properties of at least one railway component of a railway network, the method comprising: collecting at least one sensor data at least one time via a least one sensor arranged on at least one railway component at least one excitation execution position, and determining at least one characteristic eigenfrequency of the at least one railway component. This is particularly advantageous, as if multiple excitation execution positions for the force excitation are used different sub-systems or sub-components have more or less influence on the different measurements. Hereby it facilitates performing model calibration, such as, for example, when the sleeper is provided with an excitation force and the sensor is mounted on the sleeper, then the rail and its mounting clamps have less influence on this measurement. In another example, when the rail is provided with an excitation force and the sensor is mounted on the sleeper, then the rail and its mounting clamps have more influence on this measurement. The method may further comprise estimating a value of at least one physical property of the at least one railway component.
The method may comprise processing the at least one sensor data via at least one processing component to generate at least one processed sensor data.
In one embodiment, estimating the value of the at least one physical property of railway component is based on the at least one processed sensor data.
The method may comprise generating at least one railway component health status hypothesis of at least one component of the railway network.
In on embodiment, generating at least one railway component health status hypothesis of at least one component of the railway network is based on the at the at least one processed sensor data.
The method may comprise forecasting at least one railway health status of at least one component of the railway network.
The method may comprise forecasting at least one railway health status of at least one component of the railway network based on the at least one railway component health hypothesis.
The method may comprise applying an excitation force to at least one railway component of the railway network, exciting at least one dynamic response to the excitation force of the at least one railway component of a railway network, measuring the at least one dynamic response to the excitation force of the at least one railway component of the railway network, and identifying a characteristic eigenfrequency of the at least one railway component of the railway network.
The characteristic eigenfrequency of the at least one railway component of the railway network is comprised by at least one of: the at least one sensor data, and the at least one processed sensor data. The method may comprise correlating the at least one characteristic eigenfrequency with at least one railway component feature of the at least one railway component of the railway network.
The at least one railway component feature may comprise at least one geometrical feature.
The at least one railway component may comprise stiffness properties.
The at least one railway component may comprise damping properties.
The method according to any of the preceding method embodiments, wherein may comprise measuring the at least one dynamic response to the excitation force by means of the at least one sensor component.
The value of the at least one physical property of at least one railway component of the railway network may comprise at least one of: rail pad damping, rail pad stiffness, rail clamp damping, rail clamp stiffness, rail internal damping, rail shearing module, rail shearing coefficient, rail Young's modulus, rail density, rail shear tension, sleeper internal damping, sleeper shearing module, sleeper shearing coefficient, sleeper Young's modulus, sleeper density, sleeper shear tension, trackbed damping, trackbed stiffness, ballast shearing module, ballast shearing coefficient, ballast density, ballast cross-sectional area, ballast area moment of inertia.
The method according to any of the preceding embodiments, wherein the method may further comprise connecting the at least one sensor component to at least one server.
The method may comprise retrieving at least one signal comprising at least one of: the at least one sensor data, and the at least one processed sensor data.
The method may further comprise extracting at least one frequency information dataset from the at least one signal.
The at least one frequency information dataset may comprise at least one Power Spectral Density (PSD) dataset. This may be particularly advantageous, as conducting model calibration using signal characteristics, such as PSD, may be done without requiring a time intensive time-domain simulation.
The at least one frequency information dataset may comprise at least one Fast Fourier Transform (FFT) dataset. The at least one signal returns a PSD as at least one-dimensional vector of a fixed size.
The at least one signal may comprise a signal frequency content and a sampling rate, wherein the signal frequency is between 0 and 10,000 Hz, preferably between 50 and 8,000 Hz, more preferably between 50 and 5,000 Hz, and wherein the sampling rate is between 0 and 20 kHz, preferably between 0.1 and 8 kHz, more preferably between 1 and 5 kHz.
The method may comprise extracting at least one Mel spectrogram from the at least one signal.
The method may comprise mapping the data to generate a mapped dataset.
The method may comprise curating the estimating of the value of the at least one physical property of at least one railway component based on the mapped data.
The method may comprise unidirectionally connecting the at least one sensor component to the at least one server.
The method according to any of the embodiments Ml to M24, wherein the method may comprise bidirectionally connecting the at least one sensor component to the at least one server.
The method according to any of the preceding embodiment, wherein the method may comprise connecting the at least one server with the at least one processing component.
The method according to any of the preceding embodiment, wherein at least one of the at least one server may comprise at least partially one of the at least one processing component.
The method may comprise the at least one sensor component to supplying the at least one sensor data to the at least one sensor processing component.
The at least one processing component may comprise a storing component, wherein the method comprising storing at least one of: the at least one sensor data, and the at least one processed sensor data. The sensor processing component may comprise a sensor storing component configured to store at least one of: the at least one sensor data, and the at least one processed sensor data.
The at least one signal may comprise at least one of: displacement data, velocity data, and acceleration data.
The method may comprise automatically transmitting to the at least one processing component the at least one sensor data.
The method may comprise pre-processing the at least one sensor data via the at least one processing component.
The method may further comprise supplying the at least one processing component with at least one neural network component.
The method may comprise feeding into the at least one neural network component the at least one sensor data.
The method may comprise feeding into the at least one neural network component the at least one pre-processed sensor data.
The at least one neural network may comprise at least one convolutional neural network layer.
The method may comprise using the outcome of the pre-processing for predicting at least one trend of the at least one physical property of the at least one railway component.
The method may comprise teaching the at least one neural network the at least one physical property of at least one railway component using at least one training database.
The at least one railway component may comprise trackbed.
The at least one railway component may comprise at least one railpad.
The at least one railway component may comprise at least one sleeper.
The at least one railway component may comprise at least one rail. The method may further comprise generating at least one suggestion procedure to be applied on the at least one railway component.
The method may comprise generating at least one action instruction to be applied on the at least one railway component.
The method may further comprise prompting a user to implement at least one of: the at least one suggestion procedure, and the at least one action instruction.
The at least one sensor data may comprise a first sensor data and second sensor data.
The first sensor data is different from the second sensor data.
The at least one time may comprise a first time and a second time.
The first time is different from the second time.
The at least one excitation execution position may comprise a first excitation execution position and a second excitation execution position.
The first excitation execution position is different from the second excitation execution position.
The method may comprise: estimating a specific physical property of the at least one railway component to generate a first physical property finding, correlating the first physical property finding to at least health status, and generating at least one final physical property finding comprising the health status of the at least one railway component.
The at least one suggestion procedure and/or the at least one action instruction is based on the at least one final physical property finding.
The method may comprise of generating at least one causal hypothesis upon outputting the first physical property finding comprising a first health status of the at least one railway component of a railway network, outputting a second physical property finding comprising a second health status of the at least one railway component of a railway network, contrasting the first physical property finding with the second physical property finding, and predicting at least one causal event for a difference between the first physical property finding and the second physical property finding. In one embodiment, the first physical property finding and the second physical property finding relate to the same at least one railway component at least 2 different times.
The method may comprise using at least one supervised learning method.
The method may comprise using at least one unsupervised learning method.
The method may comprise automatically generating at least one interpreted dataset.
Automatically generating the at least one interpreted dataset may be performed in the at least one server.
The at least one geometrical feature may comprise a geometrical feature of at least one sleeper comprising at least one of: length, cross-sectional area, area moment of inertia, width, and height.
The at least one geometrical feature may comprise a geometrical feature of at least one sleeper comprising a distance between at least 2 sleepers.
The at least one geometrical feature may comprise a geometrical feature of at least one rail comprising at least one of: cross-sectional area of rail, area moment of inertia of rail, width of rail, height of rail
The at least one geometrical feature may comprise a geometrical feature of at least one sleeper comprising distance between at least 2 rails of a track.
The method may comprise identifying at least one of: a presence of a diverging track, mounting position of the sensor at the railway component, void under a sleeper, and presence of a movable or stiff frog.
In a third aspect the invention relates to use of the system as recited for carrying out the method as recited herein.
In one embodiment, the invention relates to the use of the method as recited herein and the system as recited herein for analyzing physical properties of at least one railway component. Furthermore, the invention relates to a computer-implemented program comprising instructions which, when executed by a user-device, causes the user-device to carry out the method as recited herein.
Additionally or alternatively, the invention relates to a computer-implemented program comprising instructions which, when executed by a server, causes the at least one server to carry out the method as recited herein.
Moreover, the invention relates to a computer-implemented program comprising instructions which, when executed causes by a user-device, causes the user-device and a server to carry out the method as recited herein.
The method may further comprise evaluating at least one of a simulation model and a model calibration procedure.
In one embodiment, estimating the at least one physical property of the railway network may be based on evaluating step.
The method may further comprise automatically generating at least one simulated sensor data.
Additionally or alternatively, the method may comprise calculating characteristics of the at least one simulated sensor data.
The model calibration procedure comprises an optimization routine.
The method comprises at least one analytical approach.
In one embodiment, the at least one analytical approach may comprise at least one of: signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
In another embodiment, the at least one analytical approach may be suitable for the model calibration procedure, the at least one analytical approach comprising at least one of: local optimization such as variations of gradient descent, Newton's method, Broyden-Fletcher- Goldfarb-Shanno algorithm, Nelder-Mead and other simplex based algorithms, Gauss- Newton, or Levenberg-Marquardt algorithm; global optimization such as evolutionary algorithms like genetic algorithm, simulated annealing, swarm intelligence, Sequential Model-based Global Optimization, Gaussian Process Approaches; machine learning optimization through reinforcement learning or pattern matching of inverse problem; and random search.
Below, system embodiments will be discussed. These embodiments are abbreviated by the letter "S" followed by a number. Whenever reference is herein made to "system embodiments", these embodiments are meant.
51. A system for monitoring physical properties of at least one railway component of a railway network, the system comprising at least one sensor component configured to sample at least one sensor data relevant to the railway network, at least one processing component configured to process the at least one sensor data, at least one storing component configured to store the at least one sensor data, and at least one analyzing component.
52. The system according to the preceding embodiment, wherein the at least one analyzing component is configured to receive the at least one sensor data from the at least one sensor component.
53. The system according to any of the preceding embodiments, wherein the system comprises an excitation component configured to provide an excitation force to at least one railway component of the railway network.
54. The system according to the preceding embodiment, wherein the excitation force comprises an impulse force.
55. The system according to the preceding embodiment, wherein the excitation force comprises a step force.
56. The system according to any of the 3 preceding embodiments, wherein the excitation force comprises a white noise. 57. The system according to any of the 4 preceding embodiments, wherein the excitation force comprises a sinusoidal sweep.
58. The system according to any of the 5 preceding embodiments, wherein the excitation force comprises a colored noise.
59. The system according to any of the 6 preceding embodiments, wherein the excitation force comprises a frequency range between 0 and 10,000 Hz, preferably between 0 and 8,000 Hz, more preferably between 0 and 2,000 Hz.
510. The system according to any of the preceding embodiments, wherein the at least one sensor component is configured to measure at least one dynamic response to the excitation force of the at least one railway component of the railway network.
511. The system according to any of the preceding embodiments, wherein the system is further configured to identify at least one characteristic eigenfrequency of the at least one railway component.
512. The system according to the preceding embodiment, wherein the at least one sensor component is configured to at least partially identify the at least one characteristic eigenfrequency of the at least one railway component.
513. The system according to any of the preceding embodiments, wherein the system is configured to estimate a value of at least one physical property of the at least one railway component.
514. The system according to any of the 2 preceding embodiments, wherein the at least one analyzing component is configured to at least partially identify the at least one characteristic eigenfrequency of the at least one railway component.
515. The system according to any of the 3 preceding embodiments, wherein the at least one analyzing component is configured to at least partially identify the at least one characteristic eigenfrequency of an interaction between at least 2 railway components of the railway network.
516. The system according to any of the 3 preceding embodiments, wherein the at least one analyzing component is configured to monitor at least one railway health status of at least one component of the railway network. 517. The system according to any of the 4 preceding embodiments, wherein the at least one analyzing component is configured to forecast at least one railway health status of at least one component of the railway network.
518. The system according to any of the preceding embodiments, wherein the at least one analyzing component is configured to generate at least one railway health status hypothesis comprising at least one cause for the at least one railway health status of the at least one component of the railway network.
519. The method according any of the preceding embodiments, wherein the at least one sensor data relevant to the railway network comprises at least one railway infrastructural feature.
520. The system according to any of the preceding, wherein the at least one analyzing component comprises a self-learning module.
521. The system according to the preceding embodiment, wherein the self-learning module is configured to analyze the at least one sensor data.
522. The system according to embodiment S19, wherein the self-learning module is configured to analyze at least one of the at least one characteristic eigenfrequency, and the at least one physical parameter.
523. The system according to any of the 3 preceding embodiments, wherein the self learning module is configured to determine changes over time of at least one of the at least one sensor data, the at least one characteristic eigenfrequency, and the at least one physical parameter.
524. The system according to any of the 4 preceding embodiments, wherein the self learning module is configured to correlate changes over time with at least one railway health status hypothesis.
525. The system according to any of the 5 preceding embodiments, wherein self-learning module is further configured to execute at least one simulation model.
526. The system according to any of the preceding embodiments, wherein the system comprises at least one computing component. 527. The system according to the preceding embodiment, wherein the at least one computing component is a remote computing component.
528. The system according to embodiment S26, wherein the at least one computing is a local computing component.
529. The system according to embodiment S26 or S28, wherein the at least one computing component is integrated in the at least one sensor component.
530. The system according to any of the 4 preceding embodiments, wherein the at least one computing component is configured to pre-process the at least one sensor data.
531. The system according to any of the 5 preceding embodiments and with features of embodiment S20, wherein the at least one computing component comprises the self learning module.
532. The system according to any of the preceding embodiments, wherein the at least one analyzing component is configured to execute at least one analytical approach.
533. The system according to any of the preceding embodiments, wherein the system further comprises at least one server.
534. The system according to the preceding embodiment, wherein the at least one server comprises a remote server.
535. The system according to any of the 2 preceding embodiments, wherein the at least one server comprises a local server.
536. The system according to the 3 preceding embodiments, wherein the at least one server is configured to receive at least one sensor data relevant to the railway network.
537. The system according to the 4 preceding embodiments, wherein the at least one server is configured to monitor at least one of the at least one sensor data, the at least one characteristic eigenfrequency, and the at least one physical property. 538. The system according to any of the preceding embodiments and with features of embodiment S33, wherein the at least one server is configured to provide at least one signal.
539. The system according to the preceding embodiment, wherein the at least one signal comprises the at least one sensor data.
540. The system according to embodiment S38, wherein the at least one signal comprises the at least one characteristic eigenfrequency.
541. The system according to embodiment S38, wherein the at least one signal comprises the at least one physical property.
542. The system according to any of the preceding embodiments and with features of embodiment S27, wherein the at least one server is configured to provide at least one signal comprising at least one railway infrastructural data.
543. The system according to any of the preceding embodiments, wherein the at least one signal is processed based on at least one analytical approach.
544. The system according to any of the preceding embodiments, wherein the at least one sensor is configured to operate in a plurality of operation modes, and wherein each operation mode is configured to monitor at least one sensor data relevant to railway network.
545. The system according to any of the preceding embodiments, wherein the at least one server comprises an interface module configured to bidirectionally communicate with at least one authorized user.
546. The system according to the preceding embodiment, wherein the interface module is configured to prompt at least one action by the at least one authorized user.
547. The system according to any of the preceding embodiments and with features of embodiment S33, wherein the at least one server is configured to forecast health status of the at least one railway network based on at least one of the at least one eigenfrequency, the at least one physical property and the at least one sensor data. 548. The system according to any of the preceding embodiments, wherein the at least one storing component is configured to store data generated by the at least one server.
549. The system according to the preceding embodiment, wherein the at least one railway component comprises at least one unmovable component, such as railway tracks.
550. The system according to any of the 2 preceding embodiments, wherein the at least one railway component comprises at least one movable component such as railway switches, frogs, rail barriers.
551. The system according to any of the preceding embodiments, wherein the processing component is configured to generate structured datasets using the at least one sensor data.
552. The system according to any of the preceding embodiments, wherein the processing component is configured to execute at least one analytical approach.
553. The system according to any of the preceding system embodiments, wherein the analyzing component is configured to execute at least one analytical approach.
554. The system according to any of the 2 preceding embodiments and with features of embodiment S33, wherein at least one server comprises at least one of the at least one processing component, and the at least one analyzing component.
555. The system according to any of the 3 preceding embodiments, wherein the system is configured to execute at least one machine learning algorithm.
556. The system according to the preceding embodiment, wherein the at least one machine learning comprises pattern recognition.
557. The system according to any of the preceding embodiments, wherein the at least one processing component is configured to (automatically) perform signal processing.
558. The system according to any of the preceding embodiments wherein the at least one sensor comprises an acceleration sensor.
559. The system according to any of the preceding embodiments wherein the at least one sensor comprises a velocity sensor. 560. The system according to any of the preceding embodiments wherein the at least one sensor comprises a displacement sensor.
561. The system according to any of the preceding embodiments wherein the at least one sensor comprises a force sensor.
562. The system according to any of the preceding embodiments wherein the at least one sensor comprises a pressure sensor.
563. The system according to embodiment S61, wherein the force sensor is arranged between a ballast and a sleeper.
564. The system according to embodiment S61, wherein the force sensor is arranged between the sleeper and a rail.
565. The system according to embodiment S62, wherein the pressure sensor is arranged between the ballast and the sleeper.
566. The system according to embodiment S62, wherein the pressure sensor is arranged between the sleeper and the rail
567. The system according to any of the preceding embodiments, wherein the at least one sensor is configured to measure data in a vertical direction.
568. The system according to any of the preceding embodiments, wherein the at least one sensor is configured to measure data in a longitudinal direction.
569. The system according to any of the preceding embodiments, wherein the at least one sensor is configured to measure data in a lateral direction.
Below, method embodiments will be discussed. These embodiments are abbreviated by the letter "M" followed by a number. Whenever reference is herein made to "method embodiments", these embodiments are meant.
Ml. A method for monitoring physical properties of at least one railway component of a railway network, the method comprising collecting at least one sensor data at least one time via a least one sensor arranged on at least one railway component at least one excitation execution position, and determining at least one characteristic eigenfrequency of the at least one railway component.
M2. The method according to the preceding embodiment, wherein the method further comprises estimating a value of at least one physical property of the at least one railway component.
M3. The method according to the preceding embodiment, wherein the method comprises processing the at least one sensor data via at least one processing component to generate at least one processed sensor data.
M4. The method according to the 3 preceding embodiments, wherein estimating the value of the at least one physical property of railway component is based on the at least one processed sensor data.
M5. The method according to any of the preceding method embodiments, wherein the method comprises generating at least one railway component health status hypothesis of at least one component of the railway network.
M6. The method according to the preceding embodiment, wherein generating at least one railway component health status hypothesis of at least one component of the railway network is based on at the at least one processed sensor data.
M7. The method according to any of the preceding method embodiments, wherein the method comprises forecasting at least one railway health status of at least one component of the railway network.
M8. The method according to the preceding embodiment, wherein the method comprises forecasting at least one railway health status of at least one component of the railway network based on the at least one railway component health hypothesis.
M9. The method according to any of the preceding method embodiments, wherein the method comprises applying an excitation force to at least one railway component of the railway network, exciting at least one dynamic response to the excitation force of the at least one railway component of a railway network, measuring the at least one dynamic response to the excitation force of the at least one railway component of the railway network, and identifying a characteristic eigenfrequency of the at least one railway component of the railway network.
M10. The method according to the preceding embodiment, wherein the characteristic eigenfrequency of the at least one railway component of the railway network is comprised by at least one of the at least one sensor data, and the at least one processed sensor data.
Mil. The method according to the preceding embodiment, wherein the method comprises correlating the at least one characteristic eigenfrequency with at least one railway component feature of the at least one railway component of the railway network.
M12. The method according to the preceding embodiment, wherein the at least one railway component feature comprises at least one geometrical feature.
M13. The method according to any of the 2 preceding embodiments, wherein the at least one railway component comprises stiffness properties.
M14. The method according to any of the 3 preceding embodiments, wherein the at least one railway component comprises damping properties.
M15. The method according to any of the preceding method embodiments, wherein the method comprises measuring the at least one dynamic response to the excitation force by means of the at least one sensor component.
M16. The method according to the preceding embodiment, wherein the value of the at least one physical property of at least one railway component of the railway network comprises at least one of: rail pad damping, rail pad stiffness, rail clamp damping, rail clamp stiffness, rail internal damping, rail shearing module, rail shearing coefficient, rail Young's modulus, rail density, rail shear tension, sleeper internal damping, sleeper shearing module, sleeper shearing coefficient, sleeper Young's modulus, sleeper density, sleeper shear tension, trackbed damping, trackbed stiffness, ballast shearing module, ballast shearing coefficient, ballast density, ballast cross-sectional area, ballast area moment of inertia. M17. The method according to any of the preceding embodiments, wherein the method further comprises connecting the at least one sensor component to at least one server.
M18. The method according to any of the preceding method embodiments, wherein the method comprises retrieving at least one signal comprising at least one of the at least one sensor data, and the at least one processed sensor data.
M19. The method according to the preceding embodiment, wherein the method further comprises extracting at least one frequency information dataset from the at least one signal.
M20. The method according to the preceding embodiment, wherein the at least one frequency information dataset comprises at least one Power Spectral Density (PSD) dataset.
M21. The method according to any of the 2 preceding embodiments, wherein the at least one frequency information dataset comprises at least one Fast Fourier Transform (FFT) dataset.
M22. The method according to any of the 4 preceding embodiments, wherein the at least one signal returns a PSD as at least one-dimensional vector of a fixed size.
M23. The method according to any of the 5 preceding embodiments, wherein the at least one signal comprises a signal frequency content and a sampling rate, wherein the signal frequency is between 0 and 10,000 Hz, preferably between 50 and 8,000 Hz, more preferably between 50 and 5,000 Hz, and wherein the sampling rate is between 0 and 20 kHz, preferably between 0.1 and 8 kHz, more preferably between 1 and 5 kHz.
M24. The method according to embodiment M18, wherein the method comprises extracting at least one Mel spectrogram from the at least one signal.
M25. The method according to any of the preceding method embodiments, wherein the method comprises mapping the data to generate a mapped dataset.
M26. The method according to the preceding embodiment, wherein the method comprises curating the estimating of the value of the at least one physical property of at least one railway component based on the mapped data. M27. The method according to any of the preceding method embodiments, wherein the method comprises unidirectionally connecting the at least one sensor component to the at least one server.
M28. The method according to any of the embodiments Ml to M26, wherein the method comprises bidirectionally connecting the at least one sensor component to the at least one server.
M29. The method according to any of the preceding embodiment, wherein the method comprises connecting the at least one server with the at least one processing component.
M30. The method according to any of the preceding embodiment, wherein at least one of the at least one server comprises at least partially one of the at least one processing component.
M31. The method according to any of the preceding method embodiments, wherein the method comprises the at least one sensor component to supplying the at least one sensor data to the at least one sensor processing component.
M32. The method according to any of the preceding method embodiments, wherein the at least one processing component comprises a storing component, wherein the method comprising storing at least one of the at least one sensor data, and the at least one processed sensor data.
M33. The method according to any of the preceding method embodiments, wherein the sensor processing component comprises a sensor storing component configured to store at least one of the at least one sensor data, and the at least one processed sensor data.
M34. The method according to any of the preceding method embodiments and with features of embodiment M18, wherein the at least one signal comprises at least one of: displacement data, velocity data, and acceleration data. M35. The method according to any of the preceding method embodiments, wherein the method comprises automatically transmitting to the at least one processing component the at least one sensor data.
M36. The method according to any of the preceding method embodiments, wherein the method comprises pre-processing the at least one sensor data via the at least one processing component.
M37. The method according to any of the preceding method embodiments, wherein the method further comprises supplying the at least one processing component with at least one neural network component.
M38. The method according to any of the preceding method embodiments, wherein the method comprises feeding into the at least one neural network component the at least one sensor data.
M39. The method according to any of the preceding method embodiments and features of embodiment M6, wherein the method comprises feeding into the at least one neural network component the at least one pre-processed sensor data.
M40. The method according to any of the preceding method embodiments and with features of embodiment M37, wherein the at least one neural network comprises at least one convolutional neural network layer.
M41. The method according to any of the preceding method embodiments and the features of embodiment M36, wherein the method comprises using the outcome of the pre processing for predicting at least one trend of the at least one physical property of the at least one railway component.
M42. The method according to any of the preceding method embodiments, wherein the method comprises teaching the at least one neural network the at least one physical property of at least one railway component using at least one training database.
M43. The method according to any of the preceding method embodiments, wherein the at least one railway component comprises trackbed.
M44. The method according to any of the preceding method embodiments, wherein the at least one railway component comprises at least one railpad. M45. The method according to any of the preceding method embodiments, wherein the at least one railway component comprises at least one sleeper.
M46. The method according to any of the preceding method embodiments, wherein the at least one railway component comprises at least one rail.
M47. The method according to any of the preceding method embodiments, wherein the method further comprises generating at least one suggestion procedure to be applied on the at least one railway component.
M48. The method according to any of the preceding method embodiments, wherein the method comprises generating at least one action instruction to be applied on the at least one railway component.
M49. The method according to any of the 2 preceding embodiments, wherein the method further comprises prompting a user to implement at least one of the at least one suggestion procedure, and the at least one action instruction.
M50. The method according to any of the preceding method embodiments, wherein the at least one sensor data comprises a first sensor data and second sensor data.
M51. The method according to the preceding embodiment, wherein the first sensor data is different from the second sensor data.
M52. The method according to any of the preceding method embodiments, wherein the at least one time comprises a first time and a second time.
M53. The method according to the preceding embodiment, wherein the first time is different from the second time.
M54. The method according to any of the preceding method embodiments, wherein the at least one excitation execution position comprises a first excitation execution position and a second excitation execution position.
M55. The method according to any of the preceding method embodiments, wherein the first excitation execution position is different from the second excitation execution position. M56. The method according to any of the preceding method embodiments and with features of embodiments M5, wherein the method comprises estimating a specific physical property of the at least one railway component to generate a first physical property finding, correlating the first physical property finding to at least health status, and generating at least one final physical property finding comprising the health status of the at least one railway component.
M57. The method according to the preceding embodiment and with features of embodiment M47 or M48, wherein the at least one suggestion procedure and/or the at least one action instruction is based on the at least one final physical property finding.
M58. The method according to any of the preceding method embodiments and with features of embodiment M56, wherein the method comprises of generating at least one causal hypothesis upon outputting the first physical property finding comprising a first health status of the at least one railway component of a railway network, outputting a second physical property finding comprising a second health status of the at least one railway component of a railway network, contrasting the first physical property finding with the second physical property finding, and predicting at least one causal event for a difference between the first physical property finding and the second physical property finding.
M59. The method according to the preceding embodiment, wherein the first physical property finding and the second physical property finding relate to the same at least one railway component at least 2 different times.
M60. The method according to any of the preceding method embodiments and with features of embodiment M7, wherein the step of forecasting at least one railway health status of the at least one component of the railway network is based on at least one analytical approach.
M61. The method according to any of the preceding method embodiments, wherein the method comprises using at least one supervised learning method.
M62. The method according to any of the preceding method embodiments, wherein the method comprises using at least one unsupervised learning method. M63. The method according to any of the preceding method embodiments, wherein the method comprises automatically generating at least one interpreted dataset.
M64. The method according to the preceding embodiment and with features of embodiment M9, wherein automatically generating the at least one interpreted dataset is performed in the at least one server.
M65. The method according to any of the preceding method embodiments and with features of embodiment M12, wherein the at least one geometrical feature comprises a geometrical feature of at least one sleeper comprising at least one of length, cross-sectional area, area moment of inertia, width, and height.
M66. The method according to any of the preceding method embodiments and with features of embodiment M12, wherein the at least one geometrical feature comprises a geometrical feature of at least one sleeper comprising distance between at least 2 sleepers.
M67. The method according to any of the preceding method embodiments and with features of embodiment M12, wherein the at least one geometrical feature comprises a geometrical feature of at least one rail comprising at least one of cross-sectional area of rail area moment of inertia of rail width of rail height of rail
M68. The method according to any of the preceding method embodiments and with features of embodiment M12, wherein the at least one geometrical feature comprises a geometrical feature of at least one sleeper comprising distance between at least 2 rails of a track.
M69. The method according to any of the preceding method, wherein the method comprises identifying at least one of a presence of a diverging track, mounting position of the sensor at the railway component, void under a sleeper, and presence of a movable or stiff frog. M70. The method according to any of the preceding method embodiments, wherein the method further comprises evaluating at least one of a simulation model, and a model calibration procedure.
M71. The method according to the preceding embodiment and with feature of embodiment M2, wherein estimating the at least one physical property of the railway network is based on evaluating step.
M72. The method according to any of the 2 preceding method embodiments, wherein the method further comprises automatically generating at least one simulated sensor data.
M73. The method according to the preceding embodiment, wherein the method comprises calculating characteristics of the at least one simulated sensor data.
M74. The method according to embodiment M68, wherein the model calibration procedure comprises an optimization routine.
M75. The method according to any of the preceding method embodiments, wherein the method comprises at least one analytical approach.
M76. The method according to the preceding embodiment, wherein the at least one analytical approach comprises at least one of: signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
M77. The method according to embodiment M75 and with features of embodiment M70, wherein the at least one analytical approach is suitable the model calibration procedure, the at least one analytical approach comprising at least one of: local optimization such as variations of gradient descent, Newton's method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Nelder-Mead and other simplex based algorithms, Gauss-Newton, or Levenberg-Marquardt algorithm; global optimization such as evolutionary algorithms like genetic algorithm, simulated annealing, swarm intelligence, Sequential Model-based Global Optimization, Gaussian Process Approaches; machine learning optimization through reinforcement learning or pattern matching of inverse problem; and random search.
Below, use embodiments will be discussed. These embodiments are abbreviated by the letter "U" followed by a number. Whenever reference is herein made to "use embodiments", these embodiments are meant.
Ul. Use of the system according to any of the preceding embodiments, for carrying out the method according to any of the preceding method embodiments.
U2. Use of the method according to any of the preceding method embodiments and the system according to any of the preceding embodiments, for analyzing physical properties of at least one railway component.
Below, program embodiments will be discussed. These embodiments are abbreviated by the letter "C" followed by a number. Whenever reference is herein made to "program embodiments", these embodiments are meant.
Cl. A computer-implemented program comprising instructions which, when executed by a user-device, causes the user-device to carry out the method steps according to any of the preceding method embodiments.
C2. A computer-implemented program comprising instructions which, when executed by a server, causes the at least one server to carry out the method steps according to any of the preceding method embodiments.
C3. A computer-implemented program comprising instructions which, when executed causes by a user-device, causes the user-device and a server to carry out the method steps according to any of the preceding method embodiments.
Brief description of the drawings
The present invention will now be described with reference to the accompanying drawings, which illustrate embodiments of the invention. These embodiments should only exemplify, but not limit, the present invention.
Fig. 1 depicts a schematic representation of a railway network and system arranged at the railway network;
Fig. 2 depicts a system for monitoring a railway network according to embodiments of the present invention;
Fig. 3 depicts a schematic of a computing device;
Fig. 4 depicts a schematic of the method according to embodiments of the present invention.
Detailed description of the drawings
In the following description, a series of features and/or steps are described. The skilled person will appreciate that unless explicitly required and/or unless requires by the context, the order of features and steps is not critical for the resulting configuration and its effect. Further, it will be apparent to the skilled person that irrespective of the order of features and steps, the presence or absence of time delay between steps can be present between some or all of the described steps.
It is noted that not all the drawings carry all the reference signs. Instead, in some of the drawings, some of the reference signs have been omitted for sake of brevity and simplicity of illustration. Embodiments of the present invention will now be described with reference to the accompanying drawings.
Fig. 1 depicts a schematic representation of a railway network and system arranged at the railway network. In simple terms, the system may comprise a railway section with the railway 1 itself, comprising rails 10 and sleepers 3. Instead of the sleepers 3 also a solid bed for the rails 10 can be provided.
Moreover, a further example of constitutional elements is conceptually represented a mast, conceptually identified by reference numeral 6. Such constitutional elements are usually arranged at or in the vicinity of railways. Furthermore, a tunnel is shown, conceptually identified by reference numeral 5. It should be understood that other constructions, buildings etc. may be present and also used for the present invention as described before and below.
For instance, a first sensor 2 can be arranged on one or more of the sleepers. The sensor 2 can be an acceleration sensor and/or any other kind of railway specific sensor. Examples have been mentioned before.
Further, a second sensor 9 can also arranged on another sleeper distant from the first sensor 2. Although it seems just a small distance in the present example, those distances can range from the distance to the neighboring sleeper to one or more kilometers. Other sensors can be used for attachment to the sleepers as well. The sensors can further be of different kind - such as where the first sensor 2 may be an acceleration sensor, the second sensor 9 can be a magnetic sensor or any other combination suitable for the specific need. The variety of sensors are enumerated before.
In one embodiment, any of the sensors, for example, the first sensor 2 and/or the second sensor 9, can directly be attached to the rail.
The sensors, for example the first sensor 2 and/or the second sensor 9, further comprise a wireless sensor network. The sensor node can transmit data to a base station (not shown here). The base station can be installed to the railway component. The base station can also be installed in the surroundings of the railway component. The base station can also be a remote base station. The communication module between the base station and the sensor node (s) can comprise, for example Xbee with a frequency of 868 MHz, but is not limited to this.
The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can also be installed in cases and inserted inside the railway component, for example inside a special hole carved into the concrete. The case can also be attached to the railway component using fixers. The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can be obtaining at least one sensor data based on acceleration, inclination, distance, etc.
The sensor node (s), for example the first sensor 2 and/or the second sensor 9, may further be divided into group, for example based on the distance. The sensor node (s), for example the first sensor 2 and/or the second sensor 9 lying within a pre-determined distance may be controlled by one base station. The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can also be installed on the moving railway component such as on-board of a vehicle. The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise an amplifier to amplify any signal received by the base station.
The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can be installed such that the sensor node lying within one group can communicate with their base station in one-hop. The base station can receive information from its 'neighbors' and retransmit all the information to the server 800.
The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise sensor(s). The sensor can be accelerometers, such as Sensor4PRI for example ADCL 345, SQ-SVS etc. The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise inclinometers, such as SQ-SI-360DA, SCA100T-D2, ADXL345 etc.
The sensor node can further comprise distance sensors. The distance sensors can be configured to at least measure the distance between slab tracks, using infrared and/or ultrasonic. The distance sensor can be for example, MB1043, SRF08, PING, etc.
The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise visual sensors, such as 3D cameras, speed enforcement cameras, traffic enforcement cameras, etc. It may be noted that sensor node(s) may comprise sensors to observe the physical environment of the infrastructure the sensor node(s) are installed in. For example, but not limited to, temperature sensor, humidity sensor, altitude sensor, pressure sensor, GPS sensor, water pressure sensor, piezometer, multi-depth deflectometers (MDD), acceleration.
The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can be installed according to a protocol based on routing trees to be able to transmit information to the base station. Once the information has been received, a cellular network can be used to send at least one sensor data to a remote server 800.
The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can comprise an analog-to-digital converter, a micro controller, a transceiver, power and memory. One or more sensor(s) can be embedded in different elements and can be mounted on boards to be attached to the railway component. The sensor node (s), for example the first sensor 2 and/or the second sensor 9, can also comprise materializing strain gauges, displacement transducers, accelerometers, inclinometers, acoustic emission, thermal detectors, among others. The analog signal outputs generated by the sensors can be converted to digital signals that can be processed by digital electronics. The data can then be transmitted to the base station by a microcontroller through a radio transceiver. All devices can be electric or electronic components supported by power supply, which can be provided through batteries or by local energy generation (such as solar panels), the latter mandatory at locations far away from energy supplies.
The at least one sensor data collected from the sensor node (s), for example the first sensor 2 and/or the second sensor 9, can be transferred to the base station using wireless communication technology such as Wi-Fi, -Bluetooth, ZigBee or any other proprietary radio technologies suitable for the purpose. For example, the ZigBee network can be advantageous to consumes less power. On the other hand, for transmitting the input data from the base station to the server 800 long-range communication such a cellular network or satellite can be used as well as wired technologies based on optical fiber.
Due to the short transmission range, communications from sensor nodes may not reach the base station, a problem that can be overcome by adopting relay nodes to pass the data from the sensor node (s), for example the first sensor 2 and/or the second sensor 9.
Another sensor 7, which may be different or the same kind of sensor, can be attached, for example, to the mast 6 or any other structure. This may be a different kind of sensor, such as, for example, an optical, temperature, even acceleration sensor, etc. A further kind of sensor, for example sensor 8, can be arranged above the railway as at the beginning or within the tunnel 5. This could, for example, be a height sensor for determining the height of a train, an optical sensor, a doppler sensor etc. It will be understood that all those sensors mentioned here and/or before are just non-limiting examples.
Furthermore, the sensors can be configured to submit the at least one sensor data via a communication network, such as a wireless communication network. As the communication network bears several advantages and disadvantages regarding availability, transmittal distance, costs etc. the transmittal of at least one sensor data is optimized as described herein before and below.
Fig. 2 depicts a system 100 monitoring a railway network. In simple terms, the system 100 may comprise a sensor component 200, a processing component 300, a storing component 400, an analyzing component 500 and a server 600.
In one embodiment, the sensor component 200 may comprise a plurality of sensor units, and each may comprise a plurality of sensor nodes. Therefore, the sensor component 200 may also be referred to as a plurality of sensor components 200. Additionally or alternatively, the sensor component may be configured to sample information relevant to a railway network, for instance, electric current based information of a given component and/part of a railway network.
In one embodiment, the processing 300 component may comprise a standalone component configure to retrieve information from the sensor 200. Additionally or alternatively, the processing component may be configured to bidirectionally communicate the storing component 300 and the analyzing component 500. For instance, the processing component 300 may transfer raw at least one sensor data to the storing component 400, wherein the raw at least one sensor data may be stored until the processing component 300 may require said data for processing to generate a processed at least one sensor data. In another embodiment, the processing component 300 may also transfer processed at least one sensor data to the storing component 400. In a further embodiment, the processing component may also retrieve data from the storing component 400.
In one embodiment, the analyzing component 500 may be configured to bidirectionally communicate with the processing component 300, the storing component 400 and/or the server 600. It will be understood that the communication of the analyzing component 500 with the other components may take place independent and/or simultaneously one from another.
In one embodiment, the processing component 300 may also be integrated with at least one of the sensors 200. In other words, the processing component 300 may also comprise an imbedded module of the sensors 200.
In embodiment, the analyzing component 500 may be configured to process at least one sensor data based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
The server 600 may comprise one or more modules configured to receive information from the analyzing component 500. In another embodiment of the presentation invention, the sensor 200, the processing component 300, the storing component 400 and the analyzing component may comprise an integrated module configured to execute subsequently the tasks corresponding to each individual component, and transfer a final processed analyzed at least one sensor data to the server 600. In simple words, in one embodiment the sensor 200, the processing component 300, the storing component 400 and the analyzing component 500 may comprises modules of a single component.
In one embodiment, the server 600 may retrieve information from the analyzing component 500, and further may provide information to the analyzing component 500, for example, operation parameters. It will be understood that each component may receive a plurality of operation parameters, for instance, the processing component 300 may be commanded to execute a preprocessing of the data received from the sensors 200.
Alternatively or additionally, the processing component 300 may be instructed to transmit the original data received from the sensors 200, i.e., the data coming from the sensors 200 can be transferred directly to the next component without executing any further task. It will be understood that the component may also be configured to perform a plurality of tasks at the same time, e.g., processing the data coming from the sensor 200 before transferring to the next component and transferring the data coming from the sensors 200 without any processing.
In one embodiment, the server 600 may comprise a cloud server, a remote server and/or a collection of different type of servers. Therefore, the server 600 may also be referred to as cloud server 600, remote server 600, or simple as servers 500. In another embodiment, the servers 500 may also converge in a central server.
It will be understood that the server 600 may also be in bidirectional communication with the storing component 400, the processing component or the sensor component 200 without passing through the analyzing component 500 or any other intermediate component. For this purpose, each component may also comprise a remote communication unit configured to establish a remote communication between a component, e.g., sensor component 200, with the server 600.
The storing component 400 may be configured to receive information from the server 600 for storage. In simple words, the storing component 400 may store information provided by the servers 600. The information provided by the server 600 may include, for example, but not limited to, data obtained by sensors 200, data processed by the processing component 500 and any additional data generated in the servers 600. It will be understood that the servers 600 may be granted access to the storing component 400 comprising, inter alia, the following permissions, reading the data allocated in the storing component 400, writing and overwriting the data stored in the storing component 400, control and modify the storage logic and the data distribution within the storing component 400.
In one embodiment of the present invention the server 600 may be configured transmit a signal to other component of the railway system based upon health status information retrieved from sensors 200. For instance, a giving health status data is provided by the server 600 and subsequently the server 600 generates a signal containing instructions, which are transmitted to the railway system for implementation. The set of instructions may comprise, inter alia, generating a hypothesis as regards the health status of the railway network and/or a failure hypothesis, which may comprise instructions to be implemented before a failure occurs on the railway network, such as switching rolling unit from on track to another. Furthermore, the signal may be based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
In one embodiment, the sensors 200 may, inter alia, adopt a configuration that allows identifying trains, their speeds and their wear effect on the tracks. The data gathered by the sensors 200 may constitute the basis for the server 600 to generate instructions for the activation of the switches. In simple words, if a train is approaching this part of the network, the sensors 200 may retrieve data that may allow activating the switches in order to redirect the trains, for example, from track 1 to track 2, according to their speed and/or wear effect. The data gathered by the sensors 200 may be communicated to the server 600, which may subsequently transmit the information and the corresponding instructions to the nearest assets, for example, the nearest switch, which may consequently be activated to control the traffic on the tracks. Furthermore, in one embodiment of the present invention, the system 100 may estimate the health status of components of the railway network and may further generate a health status and/or failure hypothesis that may allow to forecast the suitability of the component of the railway network to allocate rolling units. Such hypothesis may be based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
In another embodiment of the present invention, the system 100 may determine that a particular part and/or component of the railway network, for instance, a given section of track and/or a switch, is required to be replaced and/or maintain before a given date to avoid failure of the railway.
In one embodiment of the present invention, the system 100 may also determine that a particular rolling stock may pass through a component or portion of the railway network requiring maintenance, reparation or replacement, however, due to work schedule it may be prompt to failure if an inadequate rolling unit passes through. This approach may be advantageous, as it may allow to reduce failure of railway networks, which may be achieved by monitoring, evaluating and forecasting optimal operation conditions of the railway network.
Furthermore, the system 100 may be configured to predict a future status of the railway network and based on that may determine an optimal operation conditions using data analysis based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
In more simple words, determinations of the system 100 may directly be used forecast xxx failure, which may be advantageous for planning and execution of maintenance and/or inspections of railway network, which may further allow to minimize downtime of single machines and more importantly an adjacent railway network. Such monitoring, analyzing and forecasting may be based on machine learning comprising predicting health status hypothesis and/or failure hypothesis based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.
Fig. 3 depicts a schematic of a computing device 1000. The computing device 1000 may comprise a computing unit 35, a first data storage unit 30A, a second data storage unit 30B and a third data storage unit 30C.
The computing device 1000 can be a single computing device or an assembly of computing devices. The computing device 1000 can be locally arranged or remotely, such as a cloud solution.
On the different data storage units 30 the different data can be stored, such as the genetic data on the first data storage 30A, the time stamped data and/or event code data and/or phenotypic data on the second data storage 30B and privacy sensitive data, such as the connection of the before-mentioned data to an individual, on the thirds data storage 30C.
Additional data storage can be also provided and/or the ones mentioned before can be combined at least in part. Another data storage (not shown) can comprise data specifying for instance, air temperature, rail temperature, position of blades, model of xxx, position of xxx and/or further railway network related information. This data can also be provided on one or more of the before-mentioned data storages.
The computing unit 35 can access the first data storage unit 30A, the second data storage unit 30B and the third data storage unit 30C through the internal communication channel 160, which can comprise a bus connection 160.
The computing unit 30 may be single processor or a plurality of processors, and may be, but not limited to, a CPU (central processing unit), GPU (graphical processing unit), DSP (digital signal processor), APU (accelerator processing unit), ASIC (application-specific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (field programable gate array). The first data storage unit 30A may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).
The second data storage unit 30B may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The third data storage unit 30C may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).
It should be understood that generally, the first data storage unit 30A (also referred to as encryption key storage unit 30A), the second data storage unit 30B (also referred to as data share storage unit 30B), and the third data storage unit 30C (also referred to as decryption key storage unit 30C) can also be part of the same memory. That is, only one general data storage unit 30 per device may be provided, which may be configured to store the respective encryption key (such that the section of the data storage unit 30 storing the encryption key may be the encryption key storage unit 30A), the respective data element share (such that the section of the data storage unit 30 storing the data element share may be the data share storage unit 30B), and the respective decryption key (such that the section of the data storage unit 30 storing the decryption key may be the decryption key storage unit 30A).
In some embodiments, the third data storage unit 30C can be a secure memory device 30C, such as, a self-encrypted memory, hardware-based full disk encryption memory and the like which can automatically encrypt all of the stored data. The data can be decrypted from the storing component only upon successful authentication of the party requiring to access the third data storage unit 30C, wherein the party can be a user, computing device, processing unit and the like. In some embodiments, the third data storage unit 30C can only be connected to the computing unit 35 and the computing unit 35 can be configured to never output the data received from the third data storage unit 30C. This can ensure a secure storing and handling of the encryption key (i.e., private key) stored in the third data storage unit 30C.
In some embodiments, the second data storage unit 30B may not be provided but instead the computing device 1000 can be configured to receive a corresponding encrypted share from the database 60. In some embodiments, the computing device 1000 may comprise the second data storage unit 30B and can be configured to receive a corresponding encrypted share from the database 60.
The computing device 1000 may comprise a further storing component 140 which may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The storing component 140 may also be connected with the other components of the computing device 1000 (such as the computing component 35) through the internal communication channel 160.
Further the computing device 1000 may comprise an external communication component 130. The external communication component 130 can be configured to facilitate sending and/or receiving data to/from an external device (e.g., backup device 10, recovery device 20, database 60). The external communication component 130 may comprise an antenna (e.g., WIFI antenna, NFC antenna, 2G/3G/4G/5G antenna and the like), USB port/plug, LAN port/plug, contact pads offering electrical connectivity and the like. The external communication component 130 can send and/or receive data based on a communication protocol which can comprise instructions for sending and/or receiving data. Said instructions can be stored in the storing component 140 and can be executed by the computing unit 35 and/or external communication component 130. The external communication component 130 can be connected to the internal communication component 160. Thus, data received by the external communication component 130 can be provided to the storing component 140, computing unit 35, first data storage unit 30A and/or second data storage unit 30B and/or third data storage unit 30C. Similarly, data stored on the storing component 140, first data storage unit 30A and/or second data storage unit 30B and/or third data storage unit 30C and/or data generated by the commuting unit 35 can be provided to the external communication component 130 for being transmitted to an external device.
In addition, the computing device 1000 may comprise an input user interface 110 which can allow the user of the computing device 1000 to provide at least one input (e.g., instruction) to the computing device 100. For example, the input user interface 110 may comprise a button, keyboard, trackpad, mouse, touchscreen, joystick and the like.
Additionally, still, the computing device 1000 may comprise an output user interface 120 which can allow the computing device 1000 to provide indications to the user. For example, the output user interface 110 may be a LED, a display, a speaker and the like.
The output and the input user interface 100 may also be connected through the internal communication component 160 with the internal component of the device 100.
The processor may be singular or plural, and may be, but not limited to, a CPU, GPU, DSP, APU, or FPGA. The memory may be singular or plural, and may be, but not limited to, being volatile or non-volatile, such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM.
The data processing device can comprise means of data processing, such as, processor units, hardware accelerators and/or microcontrollers. The data processing device 20 can comprise storing components, such as, main memory (e.g., RAM), cache memory (e.g., SRAM) and/or secondary memory (e.g., HDD, SDD). The data processing device can comprise busses configured to facilitate data exchange between components of the data processing device, such as, the communication between the storing components and the processing components. The data processing device can comprise network interface cards that can be configured to connect the data processing device to a network, such as, to the Internet. The data processing device can comprise user interfaces, such as: output user interface, such as: o screens or monitors configured to display visual data (e.g., displaying graphical user interfaces of railway network status), o speakers configured to communicate audio data (e.g., playing audio data to the user), input user interface, such as: o camera configured to capture visual data (e.g., capturing images and/or videos of the user), o microphone configured to capture audio data (e.g., recording audio from the user), o keyboard configured to allow the insertion of text and/or other keyboard commands (e.g., allowing the user to enter text data and/or other keyboard commands by having the user type on the keyboard) and/or trackpad, mouse, touchscreen, joystick - configured to facilitate the navigation through different graphical user interfaces of the questionnaire.
The data processing device can be a processing unit configured to carry out instructions of a program. The data processing device can be a system-on-chip comprising processing units, storing components and busses. The data processing device can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer. The data processing device can be a server, either local and/or remote. The data processing device can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and/or user interface (such as the upper-mentioned user interfaces). Fig. 4 depicts a schematic of the method according to embodiments of the present invention according to embodiments of the present invention. In simple terms, Fig. 4 depicts an example of an implementation of the present invention, wherein the invention comprises using an excitation force 20 at least one time, for instance, at times tl, t2, and tx, wherein the excitation force 20 is applied to at least one railway component RC such as RC1, RC2 an RCx. For instance, the railway component RC can be a railway switch 12 comprising the sensor 200 mounted on the railway switch 12. The sensor 200 may measure sensor data which may be used to calculate characteristics of the sensor data. In one alternative a), the invention may further comprise a step 26 for identifying eigenfrequencies. In another alternative b), the invention may also comprise at least one of: a model calibration procedure 282, calculating step 284 for calculating characteristics of simulated sensor data and evaluation 286 of simulation railway components RC, such as the railway switch 12. Furthermore, the invention comprises identifying of health status of the railway component RC, such as the railway switch 12 by means of alternative a) and/or alternative b).
While in the above, a preferred embodiment has been described with reference to the accompanying drawings, the skilled person will understand that this embodiment was provided for illustrative purpose only and should by no means be construed to limit the scope of the present invention, which is defined by the claims.
Reference numbers and letters appearing between parentheses in the claims, identifying features described in the embodiments and illustrated in the accompanying drawings, are provided as an aid to the reader as an exemplification of the matter claimed. The inclusion of such reference numbers and letters is not to be interpreted as placing any limitations on the scope of the claims.
The term "at least one of a first option and a second option" is intended to mean the first option or the second option or the first option and the second option.
Whenever a relative term, such as "about", "substantially" or "approximately" is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., "substantially straight" should be construed to also include "(exactly) straight".
Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z). Corresponding considerations apply when terms like "after" or "before" are used.

Claims

Claims
1. A system for monitoring physical properties of at least one railway component of a railway network, the system comprising at least one sensor component configured to sample at least one sensor data relevant to the railway network, at least one processing component configured to process the at least one sensor data, at least one storing component configured to store the at least one sensor data, and at least one analyzing component.
2. The system according to the preceding claim, wherein the system comprises an excitation component configured to provide an excitation force to at least one railway component of the railway network, wherein the excitation force comprises at least one of: an impulse force, a step force, a white noise, a sinusoidal sweep, and a colored noise, wherein the at least one sensor component is configured to measure at least one dynamic response to the excitation force of the at least one railway component of the railway network.
3. The system according to any of the preceding claims, wherein the system is further configured to identify at least one characteristic eigenfrequency of the at least one railway component, wherein the at least one sensor component and/or the at least one analyzing component is configured to at least partially identify at least one of: the at least one characteristic eigenfrequency of the at least one railway component, and the at least one physical property of the at least one railway component, and the at least one analyzing component is configured to generate at least one railway health status hypothesis comprising at least one cause for the at least one railway health status of the at least one component of the railway network.
4. The system according to any of the preceding claims, wherein the system comprises at least one computing component and at least one server, wherein the at least one server is configured to receive at least one sensor data relevant to the railway network and monitor at least one of: the at least one sensor data, the at least one characteristic eigenfrequency, and the at least one physical property, and provide at least one signal comprising at least one of: the at least one sensor data, the at least one characteristic eigenfrequency and the at least one physical property.
5. The system according to any of the preceding claims, wherein the processing component is configured to execute at least one analytical approach, and automatically perform signal processing.
6. A method for monitoring physical properties of at least one railway component of a railway network, the method comprising collecting at least one sensor data at least one time via at least one sensor arranged on at least one railway component at least one excitation execution position, and determining at least one characteristic eigenfrequency of the at least one railway component.
7. The method according to the preceding claim, wherein the method further comprises estimating a value of at least one physical property of the at least one railway component, processing the at least one sensor data via at least one processing component to generate at least one processed sensor data, wherein estimating the value of the at least one physical property of railway component is based on the at least one processed sensor data, and generating at least one railway component health status hypothesis of at least one component of the railway network based on the at least one processed sensor data
8. The method according to any of the preceding method claims, wherein the method comprises applying an excitation force to at least one railway component of the railway network, exciting at least one dynamic response to the excitation force of the at least one railway component measuring the at least one dynamic response to the excitation force of the at least one railway component of the railway network, identifying a characteristic eigenfrequency of the at least one railway component of the railway network, and correlating the at least one characteristic eigenfrequency with at least one railway component feature of the at least one railway component of the railway network.
9. The method according to the preceding embodiment, wherein the characteristic eigenfrequency of the at least one railway component of the railway network is comprised by at least one of: the at least one sensor data, and the at least one processed sensor data, wherein the value of the at least one physical property of at least one railway component of the railway network comprises at least one of: rail pad damping, rail pad stiffness, rail clamp damping, rail clamp stiffness, rail internal damping, rail shearing module, rail shearing coefficient, rail Young's modulus, rail density, rail shear tension, sleeper internal damping, sleeper shearing module, sleeper shearing coefficient, sleeper Young's modulus, sleeper density, sleeper shear tension, trackbed damping, trackbed stiffness, ballast shearing module, ballast shearing coefficient, ballast density, ballast cross-sectional area, ballast area moment of inertia.
10. The method according to any of the preceding method claims, wherein the method comprises measuring the at least one dynamic response to the excitation force by means of the at least one sensor component.
11. The method according to any of the preceding claims, wherein the method further comprises connecting the at least one sensor to at least one server, retrieving at least one signal comprising at least one of: the at least one sensor data and the at least one processed sensor data, extracting at least one frequency information dataset from the at least one signal, wherein the at least one signal comprises a signal frequency content and a sampling rate, wherein the signal frequency is between 0 and 10,000 Hz, preferably between 50 and 8,000 Hz, more preferably between 50 and 5,000 Hz, and wherein the sampling rate is between 0 and 20 kHz, preferably between 0.1 and 8 kHz, more preferably between 1 and 5 kHz.
12. The method according to any of the preceding method claims, wherein the method comprises estimating a specific physical property of the at least one railway component to generate a first physical property finding, correlating the first physical property finding to at least health status, and generating at least one final physical property finding comprising the health status of the at least one railway component.
13. The method according to any of the preceding method claims, wherein the method further comprises generating at least one of: at least one suggestion procedure to be applied on the at least one railway component, and at least one action instruction to be applied on the at least one railway component, prompting a user to implement at least one of: the at least one suggestion procedure, and the at least one action instruction, wherein the at least one suggestion procedure and/or the at least one action instruction is based on the at least one final physical property finding.
14. The method according to any of the preceding method claims, wherein the method comprises of generating at least one causal hypothesis upon outputting the first physical property finding comprising a first health status of the at least one railway component of a railway network, outputting a second physical property finding comprising a second health status of the at least one railway component of a railway network, contrasting the first physical property finding with the second physical property finding, and predicting at least one causal event for a difference between the first physical property finding and the second physical property finding.
15. The method according to claim 12, wherein the method comprises evaluating at least one of: a simulation model and a model calibration procedure, wherein estimating the at least one physical property of the railway network is based on the evaluating step, automatically generating at least one simulated sensor data, and calculating characteristics of the at least one simulated sensor data.
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