CN114684217A - Rail transit health monitoring system and method - Google Patents
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Abstract
The invention relates to a rail transit health monitoring system and a rail transit health monitoring method, wherein the rail transit health monitoring method comprises the following steps: acquiring response data; preprocessing is carried out according to the response data, and vibration data to be detected are determined; and inputting the vibration data to be tested into a first neural network and/or a second neural network which are trained completely, and determining the health class of the predicted track structure and the health class of the predicted vehicle body. According to the method, after the vibration data to be measured are input to the related neural network, after the result is directly predicted, related personnel can refer to the predicted result to know the health condition and the running speed of the subway in time so as to reduce accidents.
Description
Technical Field
The invention relates to the technical field of traffic safety monitoring, in particular to a rail transit health monitoring system and a rail transit health monitoring method.
Background
In recent years, urban rail transport systems are developed rapidly, and the urban rail traffic engineering structure serving as an important structural engineering and an important component of urban traffic life lines is vital to the normal operation of cities in health service. However, the rail transit engineering structure is gradually damaged along with the operation, and particularly under the influence of complex load conditions such as temperature, train, foundation deformation and external disturbance, diseases may occur at any time, so that the driving safety is influenced. The rail transit engineering structure diseases show frequent and sudden, so that a full-time global safety monitoring system facing the rail transit engineering structure is urgently needed to be constructed, the health conditions of vehicles and the rail transit engineering structure are comprehensively and timely mastered, early warning and alarming are timely performed on potential diseases and sudden accident hidden dangers, and driving protection and navigation are guaranteed for rail transit safe operation. At present, the urban rail transit engineering structure state detection and maintenance in China are mostly in a mode of regular maintenance or fault repair afterwards, the operation maintenance and maintenance cost is high, the efficiency is low, and the real-time performance of monitoring cannot be guaranteed. Therefore, the safety monitoring technology and the management level of the rail transit engineering infrastructure are urgently needed to be improved by using a new technology and a new method. The traditional electric detection system suffers from serious electromagnetic interference on an electrified railway section, and signals cannot be transmitted in a long distance. The optical fiber sensor has the advantages of good long-term stability, remote signal transmission, easy networking, electromagnetic interference resistance and the like, gradually replaces an electric sensor with long-term stability which is difficult to ensure, and is widely applied to long-term health monitoring in the fields of bridges, tunnels, airports, railways and the like.
In the prior art, the invention patent 201110009091.1 entitled "fiber grating sensing train wheel tread state online monitoring system" discloses a train wheel health state system based on a plurality of fiber gratings, which utilizes a fiber grating strain sensor to detect the stress change of a steel rail under the coupling action of the wheel rail, and generates a smooth strain response curve when a normal wheel passes through, and when the wheel tread is abnormal, the strain response curve is distorted, and the fault reason of the wheel is judged by analyzing the distorted signal. However, the system has low detection reliability and is easy to cause false alarm, for example, when a sensing device has a problem, a strain signal is also abnormal, and wrong judgment is easy to cause; the invention patent 202010666848.3 discloses a high-speed rail safe operation detection method based on optical fiber distributed vibration monitoring, which adopts a traditional optical fiber distributed vibration detection method, and the traditional distributed optical fiber sensing technology has the disadvantages that the scattering coefficient in optical fibers is low, the signal-to-noise ratio of the system is not high, and the spatial resolution and the detection sensitivity of the distributed sensing system are further influenced. To solve this problem, researchers often employ multiple averaging algorithms or methods that artificially increase the scattering coefficient. Averaging thousands of times can improve the signal-to-noise ratio but can result in an increase in system response time. The signal-to-noise ratio can be effectively improved by improving the back scattering intensity through methods such as ultraviolet exposure or femtosecond laser processing, but the uniformity and the timeliness of the process are limited. Therefore, how to effectively monitor rail transit by using the fiber bragg grating sensor is an urgent problem to be solved.
Disclosure of Invention
In view of the above, there is a need for a rail transit health monitoring system and method, which overcome the problem in the prior art that it is difficult to efficiently use a fiber grating sensor to predict the rail transit condition.
In order to solve the above technical problem, the present invention provides a rail transit health monitoring system, including: vibration sensing network, data processing center and intelligent fortune dimension service platform that communication connection in proper order, wherein:
the vibration sensing network comprises a fiber bragg grating vibration sensing optical cable arranged on a rail transit engineering structure;
the data processing center is used for processing monitoring signals of the fiber grating vibration sensing optical cable, the fiber grating strain sensing optical cable and the fiber grating array temperature sensing optical cable and determining response data;
and the intelligent operation and maintenance service platform is used for carrying out intelligent analysis according to the response data and monitoring the track structure and the vehicle based on an intelligent analysis result.
Furthermore, the fiber bragg grating array in the vibration sensing network is made of a fiber bragg grating array sensing probe, the fiber bragg grating array sensing probe comprises a preset reflectivity grating array, and fiber bragg grating sensing points of a preset scale are multiplexed on a single optical fiber.
Furthermore, the fiber gratings on each fiber grating array sensing probe are arranged at equal intervals, the fiber gratings at equal intervals are used as nodes, and adjacent nodes form a vibration sensing unit.
Further, the vibration sensing optical cable in the vibration sensing network is closely attached to the structure to be measured, wherein:
if the structure to be detected belongs to the existing circuit, directly sticking structural adhesive to the structure to be detected, or placing a vibration sensing optical cable after slotting and then curing the vibration sensing optical cable and the structure to be detected by using cement or a curing agent;
and if the detected structure belongs to a newly repaired line, directly pouring the vibration sensing optical cable in concrete.
The invention also provides a rail transit health monitoring method, which is applied to the intelligent operation and maintenance service platform in the rail transit health monitoring system, and the method comprises the following steps:
acquiring response data;
preprocessing is carried out according to the response data, and vibration data to be detected are determined;
and inputting the vibration data to be tested into a first neural network and/or a second neural network which are trained completely, and determining the health class of the predicted track structure and the health class of the predicted vehicle body.
Further, the response data includes vibration data of a vibration pickup in an ith interval between a first time when the train approaches and a second time when the train departs, the preprocessing is performed according to the response data, and the determining of the data to be measured includes:
adding environmental noise signals to the front end and the rear end of the vibration data to determine filling data;
and carrying out data normalization processing on the filling data to determine the data to be detected.
Further, the training process of the first neural network and/or the second neural network comprises:
outputting a predicted rail structure health category when a sample set is input to the first neural network, wherein the sample set comprises sample vibration data and corresponding actual rail structure health categories;
outputting a predicted body health category when a sample set is input to the second neural network, wherein the sample set includes the sample vibration data and a corresponding actual body health category;
determining a first loss function according to the error between the predicted track structure health category and the actual track structure health category, and training a constructed first neural network to converge according to the first loss function;
and determining a second loss function according to the error between the predicted vehicle body health category and the actual vehicle body health category, and training the constructed second neural network to converge according to the second loss function.
Further, the first neural network sequentially includes: input layer, convolution layer, maximum pooling layer, Flatten layer, full connection layer and output layer, wherein:
the input layer is used for inputting the sample set;
the convolution layer is used for performing convolution operation on the sample set to determine first convolution data;
the maximum pooling layer comprises a pooling layer and a Batch Normalization layer, wherein the pooling layer is used for carrying out feature extraction and dimension reduction on the first volume data, and the Batch Normalization layer is used for carrying out standardization processing on the first volume data after dimension reduction and determining standardized data;
the Flatten layer is used for unfolding the normalized data into a one-dimensional vector;
and the full connection layer is used for reducing the dimension of the one-dimensional vector, classifying the one-dimensional vector through a softmax function and determining the predicted vehicle body health category.
Further, the second neural network sequentially includes: a BP neural network and a one-dimensional neural network, wherein:
the BP neural network trains a speed training set for outputting a predicted vehicle speed, wherein the speed training set comprises sample vibration data and a corresponding speed label;
the one-dimensional neural network is used for training the sample set through an input layer, a convolutional layer, a maximum pooling layer, a Flatten layer, a full-link layer and an output layer in sequence, and determining the predicted vehicle body health category.
Further, in the training process of the first neural network and/or the second neural network, an Adam optimizer is adopted to adaptively adjust the learning rate, and the first loss function and the second loss function are multivariate cross entropy functions.
Compared with the prior art, the invention has the beneficial effects that: firstly, response data are effectively acquired; furthermore, relevant effective data are extracted through preprocessing of the response data, vibration data to be detected are formed, and accuracy of the vibration data to be detected is guaranteed; and finally, effectively identifying and judging the vibration data to be detected through the first neural network and/or the second neural network, extracting characteristic information in the vibration data, predicting the track structure health category and the vehicle body health category, ensuring that the vibration information is quickly and efficiently based on, and realizing the multi-aspect traffic condition judgment. In summary, the invention is based on the vibration sensing network, realizes high-sensitivity acquisition of response data, and after the vibration data to be detected is input into the related neural network, the traffic operation information in various aspects can be obtained only based on the vibration data to be detected, so that the high efficiency and the rapidity of the algorithm are ensured, and after the result is directly predicted, related personnel can refer to the prediction result to know the health condition and the operation speed of the subway in time, so as to reduce the occurrence of accidents and ensure the timeliness of feedback.
Drawings
Fig. 1 is a schematic flow chart of an embodiment of a rail transit safety monitoring system provided in the present invention;
FIG. 2 is a schematic flow chart illustrating an embodiment of a rail transit health monitoring method according to the present invention;
FIG. 3 is a flowchart illustrating an embodiment of step S202 in FIG. 2 according to the present invention;
FIG. 4 is a schematic flow chart of an embodiment of a training process of a first neural network and/or a second neural network provided by the present invention;
FIG. 5 is a schematic structural diagram of a first neural network according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a BP neural network according to an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a rail transit health monitoring apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an embodiment of an electronic device provided in the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
In the description of the present invention, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to imply that the number of technical features indicated is significant. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Further, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Reference throughout this specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the described embodiments can be combined with other embodiments.
The invention provides a rail transit health monitoring system and a rail transit health monitoring method, which are characterized in that persistent data sample information is collected, a neural network is used for classification, the rail structure health and the vehicle body health are comprehensively judged, and a new thought is provided for further improving the accuracy and the efficiency of monitoring the rail transit health state.
Before the description of the embodiments, the related words are paraphrased:
an optical fiber sensor: the sensor converts the state of a measured object into a measurable optical signal. The optical fiber sensor has the working principle that light beams incident from a light source are sent into a modulator through an optical fiber, the light beams interact with external measured parameters in the modulator, so that optical properties of the light, such as intensity, wavelength, frequency, phase, polarization state and the like, are changed to form modulated light signals, and the modulated light signals are sent into a photoelectric device through the optical fiber and then are demodulated to obtain the measured parameters. In the whole process, light beams are guided in through the optical fiber and then emitted out after passing through the modulator, wherein the optical fiber is used for firstly transmitting the light beams and secondly playing a role of the optical modulator;
optical fiber array: an array is formed by mounting a bundle of optical fibers or an optical fiber ribbon on a substrate at regular intervals by using a V-Groove (V-Groove) substrate;
optical cable: fabricated to meet optical, mechanical, or environmental performance specifications, it utilizes one or more optical fibers disposed in a covering jacket as a transmission medium and can be used individually or in groups as a telecommunication cable assembly.
Based on the description of the technical terms, in the prior art, the problems that the reliability of the monitoring of the health state of the wheel by the optical fiber sensor is not high, and false alarm is easily caused exist, and the signal-to-noise ratio of the system is not high, and the response time of the system is easily overlong are easily caused.
Specific examples are described in detail below:
an embodiment of the present invention provides a rail transit safety monitoring system, and with reference to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a rail transit safety monitoring system provided by the present invention, including: vibration sensing network 1, data processing center 2 and intelligent fortune dimension service platform 3 of communication connection in proper order, wherein:
the vibration sensing network 1 comprises a fiber bragg grating vibration sensing optical cable 11 arranged on a rail transit engineering structure;
the data processing center 2 is used for processing monitoring signals of the fiber grating vibration sensing optical cable, the fiber grating strain sensing optical cable and the fiber grating array temperature sensing optical cable to determine sensing data;
and the intelligent operation and maintenance service platform 3 is used for carrying out intelligent analysis according to the sensing data and monitoring the engineering structure and the train state of the rail transit based on an intelligent analysis result.
In the embodiment of the invention, by arranging the vibration sensing network and utilizing the fiber bragg grating vibration sensing optical cable, the accuracy of monitoring vibration information is ensured, and comprehensive monitoring is realized; the data processing center is arranged to convert the monitoring information of the optical cables into sensing data; through setting up intelligent fortune dimension service platform, carry out corresponding intelligent analysis to the sensing data, follow intelligent analysis's result, effectively feed back track traffic's engineering structure and train state to this guarantees the quick monitoring to train real-time status and the operational aspect of engineering structure, and in time sends corresponding warning and handles.
As a preferred embodiment, the fiber grating arrays in the vibration sensing network are all made of fiber grating array sensing probes, each fiber grating array sensing probe includes a preset reflectivity grating array, and a fiber grating sensing point of a preset scale is multiplexed on a single optical fiber.
In the embodiment of the invention, the multiplexing capacity of the fiber grating is ensured by using the fiber grating array sensing probe, and the rail transit line of dozens of kilometers can be covered by only laying a sensing network consisting of a plurality of sensing optical cables, so that the intelligent sensing without dead zones of the whole domain is realized, and the comprehensive acquisition of monitoring signals is facilitated.
The preset reflectivity grating array is preferably an extremely weak reflectivity grating array, the fiber gratings on each fiber probe are arranged at equal intervals, the reflection bandwidth is 2-3 nm, the reflectivity consistency is good, the reflectivity range is-30 dB to-50 dB according to the multiplexing capacity of the fiber gratings on a single fiber, and the lower the reflectivity, the larger the multiplexing capacity on the single fiber.
The preset-scale fiber grating sensing points are preferably ultra-large-scale fiber grating sensing points, and various monitoring signals can be collected conveniently.
As a preferred embodiment, the fiber gratings on each fiber grating array sensing probe are arranged at equal intervals, the fiber gratings at equal intervals are used as nodes, and adjacent nodes form a vibration sensing unit.
In the embodiment of the invention, the high-sensitivity interference type vibration sensing unit is formed by utilizing the adjacent nodes, so that the vibration pickup can sense the abnormal information of the tiny vibration, and the sensitivity of system detection is ensured.
As a more specific example, the high-sensitivity interference type vibration sensing unit is formed by taking the equally spaced fiber gratings as nodes and adjacent nodes. And the pitch of the nodes of the fiber grating array is adjustable.
Particularly, the node distance l is set to be more than or equal to 3 meters. Particularly, taking the pitch of the nodes as 5 meters as an example, the minimum phase difference Δ Φ <10mrad that each vibration pickup can sense is calculated according to the formula:
in the formula: delta phi-optical signal phase difference between adjacent fiber grating nodes; λ -optical signal wavelength; delta l-the variable quantity of the grating length between adjacent grating nodes;
the minimum strain quantity which can be sensed by each vibration pickup unit is less than 50n epsilon, so that the abnormal information of tiny vibration caused by the deterioration or damage of the vehicle and the track under the coupling excitation of the wheel and the track can be sensed, and the detection capability of the system is remarkably improved.
In the embodiment of the invention, the high-sensitivity interference type vibration sensing unit is formed by utilizing the adjacent nodes, so that the vibration pickup can sense the abnormal information of the tiny vibration, and the sensitivity of system detection is ensured.
As a preferred embodiment, the vibration sensing optical cable in the vibration sensing network is closely attached to the structure to be measured, wherein:
if the structure to be detected belongs to the existing circuit, directly sticking structural adhesive to the structure to be detected, or placing a vibration sensing optical cable after slotting and then curing the vibration sensing optical cable and the structure to be detected by using cement or a curing agent;
and if the detected structure belongs to a newly repaired line, directly pouring the vibration sensing optical cable in concrete.
In the embodiment of the invention, the structure to be detected and the vibration sensing optical cable are efficiently cured by the laying method under different conditions.
As a preferred embodiment, the data processing center includes a demodulation instrument and a data acquisition network platform, wherein:
the demodulation instrument is used for converting the monitoring signals of the fiber grating vibration sensing optical cable, the fiber grating strain sensing optical cable and the fiber grating array temperature sensing optical cable into digital signals;
and the data acquisition network platform is used for acquiring the digital signals in real time, converting the digital signals into the sensing data and uploading the sensing data to the intelligent operation and maintenance service platform.
In the embodiment of the invention, the computer software and hardware and the network platform have the information acquisition function, the fiber grating array sensing optical cable is connected to the fiber grating array sensing demodulation instrument, the optical signal sensed by the sensing optical cable is converted into a digital signal through the demodulation instrument, and the computer software and hardware and the network platform are used for acquiring the monitoring data of each sensing unit in the sensing network in real time, so that the full-time global detection of the rail transit engineering structure is realized.
As a preferred embodiment, referring to fig. 2, fig. 2 is a schematic flow chart of an embodiment of a rail transit health monitoring method provided by the present invention, and includes steps S201 to S203, where:
in step S201, response data is acquired;
in step S202, preprocessing is performed according to the response data, and vibration data to be detected is determined;
in step S203, the vibration data to be measured is input to the first neural network and/or the second neural network with complete training, and the predicted rail structure health category and the predicted vehicle body health category are determined.
In the embodiment of the invention, firstly, response data is effectively acquired; furthermore, relevant effective data are extracted through preprocessing of the response data, vibration data to be detected are formed, and accuracy of the vibration data to be detected is guaranteed; and finally, effectively identifying and judging the vibration data to be detected through the first neural network and/or the second neural network, extracting characteristic information in the vibration data, predicting the track structure health category and the vehicle body health category, ensuring that the vibration information is quickly and efficiently based on, and realizing the multi-aspect traffic condition judgment.
As a more specific embodiment, the ultra-weak reflectivity fiber grating vibration sensing optical cable is laid along the length of the track structure, and the track line is divided into N sections by the fiber grating nodes:
in the above formula, L is the track line length and L is the fiber grating pitch.
The invention can collect the signals of N vibration pickers in all intervals in real time, the sampling frequency is more than or equal to 1000Hz, but the invention utilizes wheel-rail coupling excitation data, most of the real-time collection data is useless data, the storage capacity is increased, and the system resources are wasted. To facilitate data analysis and data management, it is necessary to reduce invalid data. Generally, a persistent data sample includes the following information: the interval mark i (i is more than or equal to 1 and less than or equal to N); time t0 when the train approaches section i; time t1 when the train departs from section i; vibration data continuously collected by the vibration pickups in the interval i in the time periods t0 and t 1.
Generally, the time when the vehicle has passed through the vibration pickup capable of selecting the section i-k is detected is denoted by t0, and the time when the vehicle has passed through the vibration pickup of the section i + k is detected is denoted by t1, where:
in the above formula, LcIs the train length.
As a preferred embodiment, referring to fig. 3, fig. 3 is a schematic flowchart of an embodiment of step S202 in fig. 2 provided by the present invention, where step S202 includes step S301 to step S302, where:
in step S301, adding an environmental noise signal to the front end and the rear end of the vibration data to determine padding data;
in step S302, data normalization processing is performed on the filling data to determine the data to be measured.
In the embodiment of the invention, the accuracy of the vibration data to be detected is ensured by utilizing data filling and data normalization processing so as to further ensure the accuracy of the identification result.
As a preferred embodiment, referring to fig. 4, fig. 4 is a schematic flowchart of an embodiment of a training process of a first neural network and/or a second neural network provided by the present invention, and includes steps S401 to S404, where:
in step S401, when a sample set is input to the first neural network, outputting a predicted track structure health category, wherein the sample set comprises sample vibration data and a corresponding actual track structure health category;
in step S402, when a sample set is input to the second neural network, outputting a predicted vehicle body health category, wherein the sample set includes the sample vibration data and a corresponding actual vehicle body health category;
in step S403, determining a first loss function according to an error between the predicted track structure health category and the actual track structure health category, and training a first constructed neural network to converge according to the first loss function;
in step S404, a second loss function is determined according to an error between the predicted vehicle body health category and the actual vehicle body health category, and a constructed second neural network is trained to converge according to the second loss function.
In the embodiment of the invention, the first neural network and the second neural network are effectively trained by utilizing the sample set, so that the effective identification of the rail structure health category and the vehicle body health category is completed.
As a preferred embodiment, referring to fig. 5, fig. 5 is a schematic structural diagram of an embodiment of a first neural network provided in the present invention, where the first neural network sequentially includes: input layer, convolution layer, maximum pooling layer, Flatten layer, full connection layer and output layer, wherein:
the input layer is used for inputting the sample set;
the convolution layer is used for performing convolution operation on the sample set to determine first convolution data;
the maximum pooling layer comprises a pooling layer and a Batch Normalization layer, wherein the pooling layer is used for performing feature extraction and dimension reduction on the first volume data, and the Batch Normalization layer is used for performing standardization processing on the dimension-reduced first volume data and determining standardized data;
the Flatten layer is used for unfolding the normalized data into a one-dimensional vector;
and the full connection layer is used for reducing the dimension of the one-dimensional vector, classifying the one-dimensional vector through a softmax function and determining the health category of the predicted track structure.
In the embodiment of the invention, the first neural network is constructed to realize the feature extraction of the sample set, determine the corresponding relation with the actual track structure health category and complete the network convergence so as to effectively predict the vehicle body health category.
The following describes an algorithm flow for evaluating the health of a track structure according to a specific embodiment as follows:
step one, subway signal acquisition: storing the collected data into a database;
secondly, because the speed of the subways is not consistent, when the subways enter a station and leave the station, the speed of the subways is low, and the running speed is high in the midway running process, so that the obtained signals are inconsistent in length, data filling needs to be carried out on the signals with short duration, and environmental noises are added to the front end and the rear end of the signals to enable the lengths of the signals to be consistent, wherein the lengths of the signals are 729 data points;
thirdly, data normalization: since the obtained signal amplitudes are inconsistent and uneven, which is not beneficial to the training and convergence of the neural network, it is necessary to perform normalization preprocessing on the data. The commonly used normalization methods at present are min-max normalization and Z-score normalization. The embodiment of the invention adopts Z-score standardization, and the specific formula is as follows:
where μ is the mean of all sample data and σ is the standard deviation of all sample data. The processed data are in accordance with standard normal distribution, i.e. the mean is 0 and the standard deviation is 1.
Fourthly, training and predicting the neural network: the resulting data were divided into training and test sets on a 9:1 ratio. Convolutional neural networks are used primarily for detection of track health. The main classifications are the following six categories: (1) the track is healthy; (2) breaking the rail; (3) track corrugation; (4) loosening the fastener; (5) the elastic strip is broken; (6) and (5) offline basic diseases. Convolutional neural networks tend to show better results for the problem of predicting results from large data. The convolutional neural network may be further classified into a one-dimensional convolutional neural network, a two-dimensional convolutional neural network, and the like according to a use object. The two-dimensional convolutional neural network is mainly used for the identification and classification of images. For text data, a one-dimensional convolutional neural network is more suitable. Therefore, the embodiment of the invention adopts the one-dimensional convolution neural network, the training set is used for training the neural network, and the model is continuously optimized through the adjustment of the loss function and the back propagation principle until good performance is achieved; the test set is used for evaluating the generalization ability and the orbit prediction evaluation ability of the model;
as shown in fig. 5, the multilayer optical film mainly includes an input layer, a convolutional layer (Conv1D), a Pooling layer (Pooling), a full connection layer (FC), and an output layer. The subway vibration signals are used as input tensors and sent to a neural network for training. Firstly, after passing through a one-dimensional convolutional layer, the size of a convolutional kernel is set to be 9, the number of the convolutional kernels is set to be 32, and a data tensor enters a pooling layer after passing through the convolutional layer. And the pooling layer is used for carrying out feature extraction and dimension reduction processing on the original data, so that the dimensionality of the data can be greatly reduced, and the training and convergence of the model are accelerated. The pooling layers include average pooling and maximum pooling, and in practice, the maximum pooling layer can often achieve a better effect, so the maximum pooling layer is adopted in the embodiment of the invention. To prevent overfitting of the model training, after each pooling layer, a Batch Normalization layer follows, which can normalize the data again, speed up model convergence and prevent overfitting. After three rounds of convolution and pooling operations, a two-dimensional tensor is unfolded into one-dimensional vectors through a Flatten operation, the vectors are reduced through a full-connection layer, and finally the vectors are divided into six types through a softmax function, classification results are output and used for evaluating the health condition of the track, and specific parameters are set as shown in the following table 1:
TABLE 1
To train a neural network, the training process is compiled first, with three important parameters, namely, an optimizer, a loss function, and an evaluation index. The optimizer to be adopted by the invention is an Adam optimizer, and the Adam optimizer is adopted because the learning rate can be adaptively adjusted according to different parameters, so that the Adam optimizer is more suitable for large-scale data and parameter scenes. The loss function adopts a multi-element cross entropy function, because the problem is a multi-classification problem, the method is more suitable for the multi-element cross entropy function. The specific formula is as follows:
in the above formula, y is a real label,
is a predictive tag;
as for the evaluation index, the accuracy is selected as an evaluation index for the classification performance of the model. The accuracy rate refers to the ratio of the number of samples with correct classification to the total number;
and fifthly, outputting a result, and displaying by an upper computer: after the neural network predicts the result, the result is displayed on an interface of the upper computer, and related personnel can refer to the predicted result, know the health condition of the track in time and process the track in time so as to reduce accidents.
As a preferred embodiment, with reference to fig. 6, fig. 6 is a schematic structural diagram of an embodiment of a BP neural network provided in the present invention, where the second neural network sequentially includes: a BP neural network and a one-dimensional neural network, wherein:
the BP neural network trains a speed training set and is used for outputting a predicted vehicle speed, wherein the speed training set comprises sample vibration data and a corresponding speed label;
the one-dimensional neural network is used for training the sample set through an input layer, a convolutional layer, a maximum pooling layer, a Flatten layer, a full-link layer and an output layer in sequence, and determining the predicted vehicle body health category.
In the embodiment of the invention, a second neural network is constructed to realize the feature extraction of the sample set, determine the corresponding relation between the actual vehicle speed and the actual vehicle body health category and complete the network convergence so as to effectively predict the vehicle body health category.
As a preferred embodiment, during the training process of the first neural network and/or the second neural network, an Adam optimizer is used to adaptively adjust the learning rate, and the first loss function and the second loss function are multivariate cross entropy functions.
In the embodiment of the invention, the rapidity and the effectiveness of the training process are ensured by utilizing an Adam optimizer and a multivariate cross entropy function.
The following describes a vehicle health assessment algorithm in a specific embodiment as follows:
and 3, normalizing data: since the obtained signal amplitudes are inconsistent and uneven, which is not beneficial to the training and convergence of the neural network, it is necessary to perform normalization preprocessing on the data. The current commonly used normalization methods are min-max normalization and Z-score normalization. The embodiment of the invention adopts Z-score standardization, and the specific formula is as follows:
where μ is the mean of all sample data and σ is the standard deviation of all sample data. The processed data were in accordance with the standard normal distribution, i.e. mean 0 and standard deviation 1.
And 4, predicting by a neural network: the module is divided into two parts, one part is a BP neural network, and the other part is a one-dimensional convolution neural network. The BP neural network structure is shown in fig. 6, and is used for detecting the speed of a subway. The one-dimensional convolutional neural network is used for classification of vehicle states as shown in fig. 5. The subway speed detection is a typical scalar regression problem, a model is built by utilizing the nonlinear learning capacity of a BP neural network, a training set sample with a speed label manually marked in advance is used for training, and then the speed is used for speed prediction. The model consists of an input layer, a hidden layer and an output layer. The input layer is a subway vibration signal extracted before, the hidden layer is 32 neurons, each neuron is followed by a Relu activation function, and finally, the output layer (no activation function) of one neuron is used for outputting a predicted subway speed:
wherein the output of the neuron of each hidden layer is:
f(x)＝σ(∑wx+b)
wherein, w is weight, b is deviation, σ is activation function, here Relu activation function, and its specific formula is:
f(x)＝max(0,x)
the activation function is adopted to enhance the nonlinearity of data and facilitate the training of the model;
because of the regression problem, the loss function is selected as a mean square error function (MSE), and the evaluation index is selected as a Mean Absolute Error (MAE) according to the following formula:
the optimization method comprises the following steps that an Adam optimizer is adopted, after 150 times of training, a model verification set converges to an average absolute error of 0.7, and the method can be accurately used for predicting the speed of the subway;
the one-dimensional convolutional neural network is mainly used for detecting the health condition of the subway body. The main classifications are the following four categories:
(1) vehicle health; (2) wheel flat scar defects; (3) wheel eccentricity; (4) the wheel becomes loose. Convolutional neural networks tend to show better results for the problem of predicting results from large data. The convolutional neural network may be further classified into a one-dimensional convolutional neural network, a two-dimensional convolutional neural network, and the like according to a use object. The two-dimensional convolutional neural network is mainly used for the identification and classification of images. For text data, a one-dimensional convolution neural network is more suitable;
the structure of the one-dimensional convolutional neural network is shown in fig. 5, the structure, the compiling during the training process and the selection of the loss function are consistent with those of the first neural network, but the classification labels are different, the one-dimensional convolutional neural network is subjected to dimension reduction through a full connection layer, and finally is divided into four classes through a softmax function, and classification results are output and used for the evaluation of the health condition of the subway, and are not repeated herein;
and 5, outputting a result, and displaying by an upper computer: after the neural network predicts the result, the result is displayed on an interface of the upper computer, and related personnel can refer to the predicted result to know the health condition and the running speed of the subway in time so as to reduce accidents.
An embodiment of the present invention further provides a rail transit health monitoring device, and with reference to fig. 7, fig. 7 is a schematic structural diagram of an embodiment of the rail transit health monitoring device provided in the present invention, where the rail transit health monitoring device 700 includes:
an acquisition unit 701 configured to acquire response data;
the processing unit 702 is configured to perform preprocessing according to the response data, and determine vibration data to be detected;
and the prediction unit 703 is configured to input the vibration data to be detected to the first neural network and/or the second neural network with complete training, and determine a predicted rail structure health category and a predicted vehicle body health category.
The more specific implementation manner of each unit of the rail transit health monitoring device can be referred to the description of the rail transit health monitoring method, and has similar beneficial effects, and details are not repeated here.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method for monitoring the rail transit health is implemented.
Generally, computer instructions for carrying out the methods of the present invention may be carried using any combination of one or more computer-readable storage media. Non-transitory computer readable storage media may include any computer readable medium except for the signal itself, which is temporarily propagating.
A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In an embodiment of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages, and in particular may employ Python languages suitable for neural network computing and TensorFlow, PyTorch-based platform frameworks. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Fig. 8 is a schematic structural diagram of an embodiment of the electronic device provided by the present invention, and when the electronic device 800 includes a processor 801, a memory 802, and a computer program stored in the memory 802 and capable of running on the processor 801, the processor 801 executes the computer program to implement the rail transit health monitoring method described above.
As a preferred embodiment, the electronic device 800 further includes a display 803 for displaying the processor 801 to execute the rail transit health monitoring method as described above.
Illustratively, the computer programs may be partitioned into one or more modules/units, which are stored in the memory 802 and executed by the processor 801 to implement the present invention. One or more modules/units may be a series of computer program instruction segments capable of performing certain functions, the instruction segments describing the execution of the computer program in the electronic device 800. For example, the computer program may be divided into the obtaining unit 701, the processing unit 702 and the predicting unit 703 in the above embodiments, and the specific functions of each unit are as described above and are not described herein again.
The electronic device 800 may be a desktop computer, a notebook, a palm top computer, or a smart phone with an adjustable camera module.
The processor 801 may be an integrated circuit chip having signal processing capabilities. The Processor 801 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The Memory 802 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 802 is used for storing a program, and the processor 801 executes the program after receiving an execution instruction, and the method defined by the flow disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 801, or implemented by the processor 801.
The display 803 may be an LCD display or an LED display. Such as a display screen on a cell phone.
It is understood that the configuration shown in fig. 8 is only one schematic configuration of the electronic device 800, and that the electronic device 800 may include more or less components than those shown in fig. 8. The components shown in fig. 8 may be implemented in hardware, software, or a combination thereof.
According to the computer-readable storage medium and the electronic device provided by the above embodiment of the present invention, the contents specifically described for implementing the above rail transit health monitoring method according to the present invention can be referred to, and the method has similar beneficial effects to the above rail transit health monitoring method, and details are not repeated here.
The invention discloses a rail transit health monitoring system and a method, firstly, response data is effectively obtained; furthermore, relevant effective data are extracted through preprocessing of the response data, vibration data to be detected are formed, and accuracy of the vibration data to be detected is guaranteed; and finally, effectively identifying and judging the vibration data to be detected through the first neural network and/or the second neural network, extracting characteristic information in the vibration data, predicting the track structure health category and the vehicle body health category, ensuring that the vibration information is quickly and efficiently based on, and realizing the multi-aspect traffic condition judgment.
According to the technical scheme, response data are acquired with high sensitivity based on the vibration sensing network, after vibration data to be detected are input to the related neural network, traffic operation information in various aspects can be obtained only based on the vibration data to be detected, the high efficiency and the rapidity of the algorithm are guaranteed, after the result is directly predicted, related personnel can refer to the predicted result to know the health condition and the operation speed of the subway in time, accidents are reduced, and the timeliness of feedback is guaranteed.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A rail transit health monitoring system, comprising: vibration sensing network, data processing center and intelligent fortune dimension service platform that communication connection in proper order, wherein:
the vibration sensing network comprises a fiber bragg grating vibration sensing optical cable arranged on a rail transit engineering structure;
the data processing center is used for processing the monitoring signal of the fiber bragg grating vibration sensing optical cable and determining response data;
and the intelligent operation and maintenance service platform is used for carrying out intelligent analysis according to the response data and monitoring the track structure and the vehicle based on an intelligent analysis result.
2. The rail transit health monitoring system of claim 1, wherein the fiber grating arrays in the vibration sensing network are all made of fiber grating array sensing probes, the fiber grating array sensing probes comprise preset reflectivity grating arrays, and fiber grating sensing points of preset scale are multiplexed on a single optical fiber.
3. The rail transit health monitoring system of claim 1, wherein the fiber gratings on each fiber grating array sensing probe are arranged at equal intervals, the fiber gratings at equal intervals are used as nodes, and adjacent nodes form a vibration sensing unit.
4. The rail transit health monitoring system of claim 1, wherein a vibration sensing optical cable in the vibration sensing network is in intimate contact with a structure being measured, wherein:
if the structure to be detected belongs to the existing circuit, directly sticking structural adhesive to the structure to be detected, or placing a vibration sensing optical cable after slotting and then curing the vibration sensing optical cable and the structure to be detected by using cement or a curing agent;
and if the detected structure belongs to a newly repaired line, directly pouring the vibration sensing optical cable into concrete.
5. A rail transit health monitoring method is applied to an intelligent operation and maintenance service platform in the rail transit health monitoring system according to any one of claims 1 to 4, and the method comprises the following steps:
acquiring response data of the vibration sensing network;
preprocessing is carried out according to the response data, and vibration data to be detected are determined;
and inputting the vibration data to be tested into a first neural network and/or a second neural network which are/is completely trained, and determining the health type of the predicted track structure and the health type of the predicted vehicle body.
6. The rail transit health monitoring method according to claim 5, wherein the response data includes vibration data of vibration pickers in an i-th zone between a first time when the train enters and a second time when the train leaves, and the preprocessing is performed according to the response data to determine the data to be measured includes:
adding environmental noise signals to the front end and the rear end of the vibration data to determine filling data;
and carrying out data normalization processing on the filling data, and determining the data to be detected.
7. The rail transit health monitoring method of claim 5, wherein the training process of the first neural network and/or the second neural network comprises:
outputting a predicted orbit structure health category when a sample set is input to the first neural network, wherein the sample set comprises sample vibration data and a corresponding actual orbit structure health category;
outputting a predicted body health category when a sample set is input to the second neural network, wherein the sample set includes the sample vibration data and a corresponding actual body health category;
determining a first loss function according to the error between the predicted track structure health category and the actual track structure health category, and training a constructed first neural network to converge according to the first loss function;
and determining a second loss function according to the error between the predicted vehicle body health category and the actual vehicle body health category, and training a constructed second neural network to converge according to the second loss function.
8. The rail transit health monitoring method of claim 7, wherein the first neural network comprises, in order: input layer, convolution layer, maximum pooling layer, Flatten layer, full-link layer and output layer, wherein:
the input layer is used for inputting the sample set;
the convolution layer is used for performing convolution operation on the sample set to determine first convolution data;
the maximum pooling layer comprises a pooling layer and a Batch Normalization layer, wherein the pooling layer is used for carrying out feature extraction and dimension reduction on the first volume data, and the Batch Normalization layer is used for carrying out standardization processing on the first volume data after dimension reduction and determining standardized data;
the Flatten layer is used for unfolding the normalized data into a one-dimensional vector;
and the full connection layer is used for reducing the dimension of the one-dimensional vector, classifying the one-dimensional vector through a softmax function and determining the predicted vehicle body health category.
9. The rail transit health monitoring method of claim 7, wherein the second neural network comprises, in order: a BP neural network and a one-dimensional neural network, wherein:
the BP neural network trains a speed training set for outputting a predicted vehicle speed, wherein the speed training set comprises sample vibration data and a corresponding speed label;
the one-dimensional neural network is used for training the sample set through an input layer, a convolutional layer, a maximum pooling layer, a Flatten layer, a full-link layer and an output layer in sequence, and determining the predicted vehicle body health category.
10. The rail transit health monitoring method of claim 7, wherein an Adam optimizer is used to adaptively adjust a learning rate during training of the first neural network and/or the second neural network, and the first loss function and the second loss function are multivariate cross entropy functions.
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