WO2018041569A1 - Dispositif et procédé pour déterminer l'état de voies ferrées - Google Patents

Dispositif et procédé pour déterminer l'état de voies ferrées Download PDF

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Publication number
WO2018041569A1
WO2018041569A1 PCT/EP2017/070203 EP2017070203W WO2018041569A1 WO 2018041569 A1 WO2018041569 A1 WO 2018041569A1 EP 2017070203 W EP2017070203 W EP 2017070203W WO 2018041569 A1 WO2018041569 A1 WO 2018041569A1
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WO
WIPO (PCT)
Prior art keywords
data
wear
rail
track
events
Prior art date
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PCT/EP2017/070203
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German (de)
English (en)
Inventor
Marco Nock
Original Assignee
Knorr-Bremse Systeme für Schienenfahrzeuge 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
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Application filed by Knorr-Bremse Systeme für Schienenfahrzeuge GmbH filed Critical Knorr-Bremse Systeme für Schienenfahrzeuge GmbH
Publication of WO2018041569A1 publication Critical patent/WO2018041569A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the invention relates to a method for determining the condition of railways.
  • the invention further relates to a device for determining the state of
  • the invention is therefore based on the object to provide an improved method and an improved device for detecting wear conditions of railways. This object is achieved by a method for determining the state of
  • Rail tracks are read data measured by existing sensors of at least one rail vehicle at different times and under
  • Rail vehicle can be detected on a railway. From the
  • Measurement data is determined wear conditions of the rail track.
  • the measurement data from which the wear conditions are determined are measurement data which can be detected by means of the sensor technology usually present in a normal rail vehicle. These are the sensors that are required in the vehicle for normal driving. Therefore, no additional sensors are needed, as required for example in special vehicles for track inspection.
  • the measurements are preferably made during the usual trips of
  • Rail vehicles carried over the railway track and the data in the
  • Rail vehicle recorded This may be the data from a single rail vehicle, but preferably the data collected by multiple rail vehicles.
  • the recording of the measured data, which are read in the method according to the invention, can take place at different times and under different environmental conditions. Under the environmental conditions, for example, temperatures, humidity, air pressure, but also
  • Configurations of the train such as trailer loads, the number of axles of the measuring
  • the measurements are detected when driving the rail vehicle or vehicles on a rail track. These are preferably conventional operations and
  • the measurement data can be recorded at different times, that is, either at intervals of a few hours, days, but also weeks, months or years. This also allows historical data to be taken into account when determining the state of wear of the rail track.
  • the data recorded with the rail vehicles are stored in the
  • inventive method preferably by an offline processing unit, i. not in a rail vehicle itself, but preferably in a stationary device, read. This means that the data is first extracted from the
  • Rail vehicles to the process of the invention exporting unit be transmitted. This can be done in different ways, for example through a wireless network.
  • the wear conditions of the rail track are then determined from the measured data.
  • the wear on each individual position of the rail track, and thus over the course of the rail track determined and output. This can be done, for example, by analyzing the courses of measured data when passing known wear conditions and by determining the patterns of the wearer
  • Measurement data concrete wear conditions are assigned.
  • the assignment of the wear conditions based on patterns can thus be carried out on the basis of experiments in which specific wear conditions of a route are determined or measured, and these known wear conditions are compared with the existing sensors of a rail vehicle measured course of the measured variables, characteristic of the driving over a track section with the known state of wear is.
  • the read measurement data are measurement data of acceleration sensors.
  • the measurements of existing in a vehicle will be
  • Acceleration sensors used to determine wear conditions of the rail track.
  • the acceleration sensors in particular the acceleration sensors
  • Acceleration sensors that sense accelerations in the vertical direction, i. detect perpendicular to the rail, used.
  • the acceleration sensors which measure accelerations in the transverse direction, can also be used.
  • Accelerations are usually already measured in all directions, by using SD acceleration sensors, accelerations in the longitudinal, transverse and vertical directions can also be used.
  • the measured accelerations On the basis of the measured accelerations, it is possible, for example, to detect rail fractures, track position errors or roughening of rails. In particular, based on the measured lateral accelerations, for example, dissolved
  • the acceleration sensors can, for example, to the Bogies be arranged.
  • the determination of wear conditions can be achieved, for example, by comparing measured accelerations when driving on new or normal rail tracks with measured accelerations when driving on the section to be measured
  • the read measurement data is data of at least one weighing valve of a brake system.
  • a weighing valve measures the load on the wheelset or bogie.
  • the vertical load of the rail track can be determined. If all bogies are equipped with weighing valves, then the load can also be determined in bogies.
  • the read measurement data of an air conditioner is given given environmental conditions of the number of people in the room to be air conditioned, and thus on the number of passengers in the rail vehicle to be conditioned. Based on the air conditioning performance so that the numbers of passengers can be determined or estimated.
  • the vertical load of the rail track can be determined, but on the other hand, the effect of loading the rail vehicle on the vehicle's center of gravity, and thus the impact on loads during cornering and acceleration operations, i. in a predominantly horizontal direction.
  • the data can also be provided by further sensors of the air conditioning system, for example a CO2 sensor.
  • the read measurement data is data of at least one speed sensor.
  • the vehicle deceleration or the vehicle acceleration and thus the load of the rail track in its longitudinal direction can be determined.
  • the slip can be determined using the data of the at least one speed sensor, and thus the load of the rail track in the wheel-rail contact, ie the punctual wear.
  • the read measurement data are data of the sensor system of at least one bogie. This may be, for example, data of one or more arranged on the bogie
  • Refresh rate depends on the speed of the rail vehicle.
  • events are detected that have a special influence on the wear of the rail track, and the
  • Wear conditions of the rail track are determined taking into account the events that have a particular influence on the wear of the rail track.
  • Rail vehicle are not regularly or with less likelihood, and in particular on the normal roles of a wheel on a rail
  • wear phenomena caused by these singular events can in principle also be determined, for example, from measurement data recorded by acceleration sensors. However, the accuracy of determining wear conditions can be increased if such events are taken into account directly in the wear determination. In addition, this allows a comparison between the measurements of, for example, acceleration sensors, and the known for these positions singular events, so that patterns can be determined, showing how a measurement of an acceleration sensor for a position of a rail track, where such a singular event has taken place.
  • the method for determining wear states from the measured data and the assignment of wear conditions to observed progressions of the measured variables can be improved.
  • as events which have a particular influence on the wear of the rail track one or more of the events slip-on slip or spin of drive wheels,
  • Wear condition can be improved.
  • this information about the particular events can be compared with the further measurement data, in particular data from acceleration sensors, in order to increase the accuracy of the determination of wear conditions and
  • position data are assigned to the measured data after the data have been read in.
  • the assignment of position data can also already be done in the measuring vehicle by a measurement data record with, for example, from a GPS system position data obtained, ie provided with a place stamp. Irrespective of whether the assignment of position data already takes place during the measurement in the vehicle or only in the method according to the invention during the evaluation, each measured value can thus be assigned a position, ie a position on the route at which the measurement is taken.
  • data of the environment of the rail track are taken into account when determining the wear conditions.
  • temperatures, pressures or humidity or the specific location such as the location in a tunnel or in a slope
  • the current state of wear of the rail track is determined. This makes it possible to assess which wear is present at which position of the rail track. As a result, maintenance recommendations, speed recommendations, etc. can be derived. This allows a
  • the development of the state of wear of the track is predicted from the measured data.
  • the previous number of train movements or the tensile loads, or under a modified stress, such as an increase in the tensile density will develop.
  • measurement data from the past can be used, so that a comparison of current and earlier measurement data can be used to determine a development of the wear of the track and from this a prediction of future wear development
  • the determination of the wear conditions of the rail path assigned to the position data takes place by means of big data methods, for example artificial neural networks.
  • Big-data methods also referred to as mass data methods in Germany, describe methods that can be used to evaluate large and / or complex and / or weakly structured and / or rapidly changing amounts of data.
  • the artificial neural networks are first to be trained on the basis of data in which measurement data that passes when a motor vehicle passes over it Track section are measured with a known state of wear, the known state of wear or this state characterizing variables
  • measurements are to be carried out, for example by means of test vehicles, in which the wear of the rail is measured directly, for example by measuring the roughness depth of the rail.
  • This training data thus assign measured wear to the measurement data that the sensor system present in the vehicle outputs when driving over a section of track with such a state of wear. This allows the artificial neural networks to be trained.
  • railway track by means of artificial neural networks is then assumed by a trained neural network.
  • the trained artificial neural network then obtains the measured data from the sensors of the vehicle as input data when determining the state of wear from the measured values when using the method and then outputs the state of wear to a respective position of the rail path. Due to the artificial neural networks, it is possible to evaluate the measured data with comparatively low computing power and with a relatively low computing power from the measured data the state of wear of the
  • preprocessing of the data is performed separately for the respective ones
  • the events that have special influence on the wear of the railway for example, the use of
  • Vehicle such as acceleration sensors
  • the measurement data can be assigned the respective position, as far as this is not already done in the vehicle.
  • Rail vehicle recorded on a rail measurement data and means for determining wear conditions of the rail track from the measured data.
  • the measured data are thus carried out, as in the description of the method carried out by sensors that are standard on a rail vehicle and are required for normal driving.
  • the vehicle is not equipped with special sensors, such as special vehicles, which are specially required for monitoring and recording the condition of railways.
  • the device according to the invention has means with which these measured data can be read.
  • the measured data can come from one and in particular from several rail vehicles.
  • the measurement data are preferably read offline in a stationary device and processed. If it is in particular data of a single vehicle, the reading and the processing can also be done on board the vehicle, for example in real time.
  • the measurement data are read in the device and stored.
  • the measurement data are data that can be acquired by one or more vehicles, in particular at different times and under different environmental conditions. Thus historical data, that is data from measurements taken at different time intervals, can also be taken into account.
  • the device also has means with which the wear conditions of the rail track can be determined from the measured data. Under wear, for example, the roughness or waviness of the rail surface, for example, when corrugation, or local damage such as rail break understood.
  • the device has means for assigning position data to the measurement data.
  • the position data can be detected in the vehicle by GPS, for example, and can already be assigned to the measurement data in the vehicle.
  • the measurement data are then already provided in the vehicle in addition to a time stamp with a place stamp.
  • the assignment of the position data to the measured data can also be made offline. By assigning the position data, it is possible to give a measurement a specific position along the railroad
  • the device further comprises means for reading and storing events that have a special influence on the wear of the rail track, as well as means for determining
  • Wear conditions of the rail track which are assigned to the position data, taking into account the events that have a special influence on the wear. This makes it possible to carry out special events which occur only rarely during normal driving but which lead to a high degree of wear of the rail track, such as sanding, starting slippage or spinning of drive wheels, for example.
  • Anti-slip protection or the use of rail brakes to be considered.
  • this makes it possible, on the one hand, to increase the accuracy of the determination of the wear conditions, and on the other hand, to correlate the measurement data, for example measurements of acceleration sensors, with the events, which have a particular influence on the wear, so that patterns are determined can be obtained from the measurements with those existing in the rail vehicle Sensors to the wear by the special singular events close.
  • the device is designed so that it can determine the wear of the rail travel exclusively or predominantly from data provided by existing in the rail vehicle acceleration sensors. These are preferably
  • Acceleration sensors that detect vertical accelerations, i. Detect accelerations in the direction of the rail. These acceleration sensors may preferably be arranged on the bogie.
  • the sensors are the sensors that are standard in the vehicle to ensure driving. There is thus no need for sensors that serve no purpose other than wear detection.
  • the device is designed so that it can determine the wear of the rail track using data provided by at least one present in the rail vehicle weighing valve, and / or by an existing in the rail vehicle air conditioning and / or existing in the rail vehicle Speed sensor and / or by the sensor system of at least one existing in the rail vehicle bogie.
  • the device has means to predict the development of the state of wear of the track. This makes it possible not only to recognize the current state of wear from the measured data, but also to make predictions about the future development and, based on the current and the predicted state of wear, recommendations for maintenance or driving, for example in the form of
  • Wear state can be done in the device by means that evaluate both current measurement data as well as past measurement data taken over different periods of time in the past.
  • the device is suitable for making statements about a future change in the wear of the track. This allows using the predicted wear development of the rail Recommendations for maintenance and repairs, for example, for the grinding of rails derived.
  • Fig. 1 shows the procedure for determining wear conditions of a
  • Fig. 2 shows the procedure for determining wear conditions of
  • step S10 measurement data originating from vehicle sensors are read in in step S10. These data come from sensors that are part of the standard equipment of the vehicle and are required for normal vehicle operation or diagnostics of the vehicle components. This is not data captured by special sensors, such as those used in special vehicles for measuring
  • the read measurement data can be, for example, measurement data from acceleration sensors. If, for example, 3D acceleration sensors are installed in the vehicle, data measured by these sensors can be used. But also the data of only one spatial direction measuring acceleration sensors, in particular for measuring the vertical acceleration and / or the lateral acceleration can be used.
  • the data may also include data from brake control weighing valves, air conditioning data, speed sensor data or data from the
  • Bogie sensors act.
  • the data is preferably data from multiple vehicles taken at different times and under different conditions. This also allows data from the past to be used whereby the wear of the railway track in the past can be analyzed and observed up to the present time as well as prognoses can be made.
  • the recording of the measured data can take place at different times and under different environmental conditions, for example at different temperatures, humidities, air pressures, but also at different temperatures
  • Configurations of the train for example a variation of towed loads, number of axles of the measuring rail vehicle etc.
  • the data of the vehicle sensors are transmitted from the vehicle to the device that performs the evaluation.
  • the evaluation steps are preferably performed by an off-line processing unit, i. one not in one
  • Rail vehicle itself, but preferably in a stationary
  • Evaluation device which is located outside the rail vehicle carried out. In the establishment, the evaluation and determination of the
  • Wear state of the data performs it is thus preferably a stationary offline device.
  • the data must therefore be transmitted from the vehicles to the facility. This can be done by wireless communication, but also on other transmission paths.
  • the data is temporarily stored, for example, in the vehicle or in other stationary facilities.
  • the vehicles and cached can be transmitted in various ways of data transmission, for example via radio or via temporary storage in an in-vehicle or in an external memory, to the device which determines the state of wear.
  • events are recorded which have a particular influence on the wear of the rail track.
  • These events may include, for example, the occurrence of start-up slip, spin of drive wheels, sanding, anti-skid deployment, or use of rail brakes. These events are those to events that cause a particularly high wear of the rail track, and in particular the rail surface.
  • the information base for determining the state of wear is improved. This information about the particular events can be compared with the further measurement data, in particular the data measured by means of acceleration sensors, in order to increase the accuracy of the determination of wear conditions and to correlate the further measurement data with these wear events.
  • patterns can be detected which are characteristic of the singular events mentioned, so that it can be concluded even without knowledge of the wear events from the data of acceleration sensors, for example, whether at a position a slip, a spin of drive wheels or a another event took place.
  • the measurement data and the wear events may be optional in step S14
  • Position data to be assigned Insofar as position data have already been assigned to the measured data and the wear event information in the vehicle, for example from GPS systems of the vehicle, a subsequent assignment of
  • step S16 out of the measurement data and the wear events
  • Wear conditions are analyzed and assigned to the measured patterns of the measured data specific wear conditions. The assignment of
  • Wear conditions based on patterns can thus take place on the basis of tests in which concrete wear conditions of a section are determined or measured, and these known wear conditions are compared with a course of the measured variables measured with the existing sensors of a rail vehicle. which is characteristic for driving over a section of track with the known state of wear.
  • the determination can be made on the basis of artificial neural networks which are trained in such a way that they can be calculated from the measured data and the
  • the measured accelerations, rail fractures, track position errors or roughening of rails can be detected, for example.
  • the measured lateral accelerations for example, dissolved rail brackets or screw connections or displacements of the
  • acceleration sensors are used for the measurements, they can be arranged, for example, on the bogies.
  • the determination of wear conditions can be carried out, for example, by comparing measured accelerations when driving on new or normal rail tracks with measured accelerations when driving on the section to be measured.
  • a preprocessing of the data can be carried out separately for the respective rail vehicles. This preprocessing can also already by a in the
  • the wear state determined in step S16 can be processed and output, for example in graphical representation. Maintenance recommendations or recommendations for driving can be issued. Furthermore, as far as wear events and data from vehicle sensors, which originate from past periods, can be used in the determination of the wear conditions, a representation of the development of the state of wear for the
  • FIG. 2 shows a method sequence in which the preprocessing of the vehicle data is first carried out.
  • acceleration sensors that accelerate to individual
  • Components preferably in the vertical direction to the rail, measure. These may be, for example, acceleration sensors, which are arranged on bogies of the rail vehicle. Furthermore, special wear events 21, such as the use of the rail brake, sanding or start-up slip, are recorded in the vehicle. These data 20, 21 are assigned, for example in the vehicle from a GPS system position data, so that the measured values and the
  • Wear events positions on the rail can be clearly assigned.
  • the data 20, 21 are transmitted from the rail vehicles to the device, which determines the state of wear from the data. As already stated above, this can be done by wireless communication, or by a separate one
  • Data acquisition device which caches the data, and from which the data can be retrieved by the device, which determines the state of wear as needed.
  • a preprocessing 22 of the data originating from the individual vehicles is undertaken. It can For example, special features of the individual vehicles are taken into account, for example, sensor types or sensor positions.
  • preprocessing data can be filtered, the amount of data can be reduced, and position data can be assigned to the data, if not already done during the measurement in the vehicle.
  • Wear 24 done.
  • historical data 25 in which measurements but also wear conditions at earlier times are stored, can be used.
  • further data sources 26 can be used for the analysis, the evaluation and the prognosis, in which, for example, patterns are stored in which concrete wear conditions are associated with those for them
  • Wear conditions are correlated by the acceleration sensor 20 of the vehicle measured progressions of the measured values.

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Abstract

L'invention concerne un procédé pour déterminer l'état de voies ferrées, selon lequel des données de mesure (20, 21) acquises dans différentes conditions environnementales et à différents moments au moyen d'un système de détection présent dans au moins un véhicule ferroviaire sont lues, lesdites données ayant été acquises pendant que ledit au moins un véhicule ferroviaire circule sur une voie ferrée, et des états d'usure (24) de la voie ferrée sont déterminés à partir de ces données de mesure (20, 21). L'invention concerne également un dispositif permettant de déterminer l'état de voies ferrées.
PCT/EP2017/070203 2016-09-02 2017-08-09 Dispositif et procédé pour déterminer l'état de voies ferrées WO2018041569A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102016116415.1A DE102016116415A1 (de) 2016-09-02 2016-09-02 Vorrichtung und Verfahren zum Ermitteln des Zustands von Schienenwegen
DE102016116415.1 2016-09-02

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WO2018041569A1 true WO2018041569A1 (fr) 2018-03-08

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PCT/EP2017/070203 WO2018041569A1 (fr) 2016-09-02 2017-08-09 Dispositif et procédé pour déterminer l'état de voies ferrées

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Cited By (1)

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EP3954592A3 (fr) * 2020-08-15 2022-03-09 Hermann Hamberger Procédure de détermination et d'évaluation des dysfonctionnements du système de voie des véhicules dans le cadre de l'exploitation ferroviaire normale

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IT201700100433A1 (it) * 2017-09-07 2019-03-07 Faiveley Transport Italia Spa Procedimento di controllo di un sistema frenante di almeno un veicolo ferroviario.
CN109017867B (zh) * 2018-08-01 2021-05-25 湖南大学 钢轨波磨动态测量方法
CN111874039A (zh) * 2020-08-10 2020-11-03 中车长春轨道客车股份有限公司 车辆防滑控制方法、第一防滑主机、第二防滑主机及系统
DE102021202643A1 (de) 2021-03-18 2022-03-17 Zf Friedrichshafen Ag Verfahren zur Zustandsüberwachung einer Gleisanlage und/oder einer Schienenfahrzeugkomponente und Zustandsüberwachungssystem
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EP3954592A3 (fr) * 2020-08-15 2022-03-09 Hermann Hamberger Procédure de détermination et d'évaluation des dysfonctionnements du système de voie des véhicules dans le cadre de l'exploitation ferroviaire normale

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