EP4124539A1 - Procédé de comptage des essieux à évaluation assistée par ordinateur - Google Patents

Procédé de comptage des essieux à évaluation assistée par ordinateur Download PDF

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Publication number
EP4124539A1
EP4124539A1 EP21188561.1A EP21188561A EP4124539A1 EP 4124539 A1 EP4124539 A1 EP 4124539A1 EP 21188561 A EP21188561 A EP 21188561A EP 4124539 A1 EP4124539 A1 EP 4124539A1
Authority
EP
European Patent Office
Prior art keywords
measurement signal
course
counting sensor
axle counting
normalization
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.)
Granted
Application number
EP21188561.1A
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German (de)
English (en)
Other versions
EP4124539B1 (fr
EP4124539C0 (fr
Inventor
Jens Braband
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.)
Siemens Mobility GmbH
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Siemens Mobility 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 Siemens Mobility GmbH filed Critical Siemens Mobility GmbH
Priority to EP21188561.1A priority Critical patent/EP4124539B1/fr
Priority to CN202210895177.7A priority patent/CN115675568A/zh
Priority to AU2022209303A priority patent/AU2022209303B2/en
Publication of EP4124539A1 publication Critical patent/EP4124539A1/fr
Application granted granted Critical
Publication of EP4124539B1 publication Critical patent/EP4124539B1/fr
Publication of EP4124539C0 publication Critical patent/EP4124539C0/fr
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L1/00—Devices along the route controlled by interaction with the vehicle or train
    • B61L1/16—Devices for counting axles; Devices for counting vehicles
    • B61L1/162—Devices for counting axles; Devices for counting vehicles characterised by the error correction
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L1/00—Devices along the route controlled by interaction with the vehicle or train
    • B61L1/16—Devices for counting axles; Devices for counting vehicles
    • B61L1/161—Devices for counting axles; Devices for counting vehicles characterised by the counting methods
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L25/00—Recording or indicating positions or identities of vehicles or trains or setting of track apparatus
    • B61L25/02—Indicating or recording positions or identities of vehicles or trains
    • B61L25/021—Measuring and recording of train speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L25/00—Recording or indicating positions or identities of vehicles or trains or setting of track apparatus
    • B61L25/02—Indicating or recording positions or identities of vehicles or trains
    • B61L25/023—Determination of driving direction of vehicle or train

Definitions

  • the invention relates to a method for counting axles, in which an axle counting sensor mounted on a track is passed by a wheel, the axle counting sensor generates a measurement signal, the course of the measurement signal is evaluated with the aid of a computer, the wheel being identified.
  • the invention also relates to a computer program product and a provision device for this computer program product, the computer program product being equipped with program instructions for carrying out this method.
  • a related problem needs to be solved, for example, in computer-aided handwriting recognition. Describes how to recognize handwriting Claus Bahlmann et al. in IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 26, NO. 3, MARCH 2004 in the paper "The Writer Independent Online Handwriting Recognition System frog on hand and Cluster Generative Statistical Dynamic Time Warping " is a suitable method. The aim here is to recognize letters despite the differences resulting from different handwritings. However, this cannot be easily transferred to axle counters, since axle counters have to distinguish between useful signals indicating wheel passage and interference signals.
  • a bogie consists of two consecutive wheels, i.e. two maxima of the signal amplitude of the measurement signal with a certain plateau in between. This can result in a measurement error known as skewering. In what is known as the spit run, the aforementioned plateau can be so high that a third wheel is incorrectly detected.
  • the object of the invention is to specify a method for counting axles which has a comparatively high level of security against incorrect identification of wheel passages.
  • the object of the invention consists in specifying a computer program product and a provision device for this computer program product, with which the aforementioned method can be carried out.
  • the measurement signal is a time profile of the measured variable, preferably the signal voltage, which has respective maxima due to the wheel passing by an axle, but also due to interference. This means that the event to be recorded can be recognized by the computer-aided evaluation of the measurement signal, that a wheel the axle counting sensor has happened, however, interference signals can also be incorrectly recognized as such wheel passage.
  • both an amplitude normalization and a dynamic time normalization also referred to as dynamic time warping (hereinafter referred to as DTW for short) are applied to the measurement result.
  • DTW dynamic time warping
  • the amplitude normalization proceeds in such a way that the maximum of the observed course of the measurement signal after normalization is identical to a specified target value. It can preferably be normalized to 1, i. H. that the target value is equal to 1. However, this is not absolutely necessary. It is important that the specified target value of the maximum matches the maxima that are contained in the patterns with which the relevant profile of the measurement signal is to be compared.
  • the DTW is carried out in order to identify in a time-limited section of the entire course of the measurement signal, which extends before and after the maximum, which is to be compared with patterns for the purpose of detecting wheel spin or faults that occur. Since a wheel run produces a signal curve that rises to a maximum and then falls again, a maximum is contained in the curves identified by the DTW.
  • a time normalization is carried out for the purpose that a comparison of the relevant course of the measurement signal can be carried out with the samples.
  • the course of the measurement signal in particular from depends on the speed of the vehicle passing the axle counting sensor. Higher speed produces a steeper, shorter rise to the maximum (and then a corresponding fall). A slower speed, in comparison, produces a flatter, longer rise to the maximum (and then a corresponding fall).
  • DTW The principle of DTW is known, for example, from speech recognition (the recognition of speech characteristics when dictating):
  • speech recognition the recognition of speech characteristics when dictating
  • individual words from a spoken text are to be recognized by comparison with stored speech patterns.
  • One problem is that the words are often pronounced differently. Vowels in particular are often pronounced longer or shorter.
  • the word should be stretched or compressed accordingly, but not evenly, but mainly on the vowels that were spoken longer or shorter.
  • the dynamic time warping algorithm performs this adaptive time normalization.
  • Another use case is handwriting recognition.
  • a pattern recognition of individual letters takes place, the aim being to recognize the letters in different handwritings.
  • the invention makes use of the knowledge that the measurement signals of an axle counter have a comparatively low level of complexity compared to character recognition or speech recognition. On the other hand, however, there are errors that can be confused with a wheel run and therefore lead to incorrect results in the evaluation. Despite the comparatively low complexity of the patterns, these must be reliably recognized. This is where the invention comes in, in that patterns are not only defined for the events to be recognized in a wheel passage of different vehicles, but also for errors that typically occur, which are then recognized as such and cannot be confused with wheel passage.
  • the invention aims not only to recognize the events whose occurrence is desired and to be counted, but also to consciously recognize the events which should not occur, therefore should not be counted, but could be incorrectly recognized as an event to be counted. If these events are reliably recognized as errors, they can be excluded as counting events, even if their evaluation as a wheel run event to be counted would be uncertain.
  • the added value according to the invention of an increase in the recognition reliability.
  • “computer-aided” or “computer-implemented” can be understood to mean an implementation of the method in which at least one computer or processor executes at least one method step of the method.
  • Computers can be, for example, personal computers, servers, handheld computers, mobile phones and other communication devices that process computer-aided data, processors and other electronic devices for data processing, which can preferably also be combined to form a network.
  • a “processor” can be understood to mean, for example, a converter, a sensor for generating measurement signals, or an electronic circuit.
  • a processor can in particular be a main processor (Central Processing Unit, CPU), a microprocessor, a microcontroller, or a digital signal processor, possibly in combination with a memory unit for storing program instructions, etc.
  • CPU Central Processing Unit
  • a processor can also be understood to mean a virtualized processor or a soft CPU.
  • a “memory unit” can be understood to mean, for example, a computer-readable memory in the form of a random-access memory (RAM) or data memory (hard disk or data carrier).
  • RAM random-access memory
  • data memory hard disk or data carrier
  • interfaces can hardware, for example, wired or wireless connection, and / or software, for example as an interaction between individual program modules or program parts of one or more computer programs.
  • Program modules are to be understood as meaning individual functional units which enable a program sequence of method steps according to the invention. These functional units can be implemented in a single computer program or in several computer programs that communicate with one another. The interfaces implemented here can be implemented in terms of software within a single processor or in terms of hardware if multiple processors are used.
  • the profile of the measurement signal normalized by amplitude normalization and time normalization is compared with patterns of both at least one profile for the measurement signal when a single wheel is passed and at least one profile for the measurement signal when two wheels of a bogie are passed.
  • This refinement of the invention makes use of the knowledge that the double axle of a bogie, ie the two wheels which pass the axle counting sensor in this case, result in a characteristic pattern with two maxima. If these two maxima are identified by the DTW as belonging to the bogie, a normalization can be carried out with reference to this double event. This can then be compared with the associated template. This achieves a further increase in reliability. A detected bogie therefore counts twice for an axle count, since it has two axles.
  • the two wheels can also be identified and evaluated as individual wheels.
  • the counting result is the same provided that both wheels are recognized. This shows that with the definition of patterns that belong to a bogie, an additional identification option is created, with the effect that the recognition reliability is improved. This is due to the fact that the pattern of a bogie provides more characteristic evaluation criteria and can therefore be identified more easily. However, if it is not recognized as a bogie, there is still the fallback position of recognizing the individual wheels.
  • a comparison with patterns of a profile for the measurement signal when passing two wheels of a bogie is only carried out if the time offset of the maxima in the profile of the measurement signal is dependent on the speed of a wheel passing the axle counting sensor Vehicle specified limit does not exceed.
  • This measure is based on the knowledge that when passing a bogie, the axle counting sensor records two maxima in quick succession. In other words, it can be ruled out that a bogie is involved if the maxima are not measured within a speed-dependent time interval that is characteristic of bogies.
  • the speed of the vehicle crossing the axle counting sensor must be known. There are different ways to do this.
  • the speed can be determined, for example, by means of another sensor and fed into the method as an input variable.
  • the speed in the vehicle can be measured and transmitted by radio to a computer that carries out the calculations of the method according to the invention.
  • Another possibility is to estimate the speed from the context of a pattern of maxima (corresponding to the axle counting pulses).
  • Bogies are usually installed on vehicles of a certain length, so that bogies each produce maxima that are close together and then a longer pause (passing the middle of the vehicle) or a shorter pause (between two coupled vehicles) occurs.
  • the speed can be estimated from the ratio of the pauses and thus also determine the speed-dependent limit value.
  • double axle counters in which two axle counting sensors are installed in quick succession. Since the distance between the axle counting sensors is known, the speed can be deduced by determining the time offset of the maxima generated by the same wheel in the two axle counting sensors (more on this below).
  • the profile of the measurement signal normalized by amplitude normalization and time normalization is compared with at least one pattern of a profile for the measurement signal when a gauntlet occurs when bogies are cornering.
  • a skewer is a measurable signal increase in the measurement signal of an axle counting sensor, which generates a maximum between the two maxima of the wheel passages of a bogie.
  • a skewer occurs primarily when the axle counting sensor is installed in a curve and the measurement takes place while the bogie is cornering.
  • gauntlet effects are defined as patterns of errors that occur, then if a gauntlet course occurs during the measurement by an axle counting sensor within the scope of the DTW, a course can be generated which, after comparison with the pattern available for the gauntlet course, can be assigned to this error. If this assignment is unambiguous, the relevant course of the measurement signal can be excluded from an assignment of the event of wheel passage. This is particularly advantageous if an assignment to a wheel passage would be borderline and, in case of doubt, a non-existent axle would be incorrectly counted.
  • an axle counter which has a first axle counting sensor and a second axle counting sensor arranged one behind the other in the direction of travel, with the method being run through successively for the first axle counting sensor and the second axle counting sensor.
  • the two built-in axle counting sensors i.e. the first axle counting sensor and the second axle counting sensor, therefore generate the same maxima in quick succession in the course of the measurement signal over time, at least when there are no faults. In this case, the maxima correspond to the counted wheels. Otherwise, interference signals can also be detected, which lead to maxima.
  • axle counting sensor does not change the functional principle of the axle counter.
  • the first axle counting sensor and the second axle counting sensor work in exactly the same way as the axle counting sensor of an axle counter in which only one axle counting sensor is installed.
  • the statements made in connection with this invention therefore apply equally to the axle counting sensor or the first axle counting sensor and the second axle counting sensor, unless otherwise described.
  • first axle counting sensor and a second axle counting sensor have the advantage that the axle counter is more secure against failure.
  • the sensor signals can also be used to determine the speed of the vehicle passing the axle counter. The maxima generated by one and the same wheel are examined with regard to their time offset in the first axle counting sensor and in the second axle counting sensor and, taking into account the known distance between the first axle counting sensor and the second axle counting sensor, the speed certainly.
  • the maxima in the first profile recorded by the first axle counting sensor and the second profile recorded by the second axle counting sensor are compared and only those maxima in the first profile and second profile of the measurement signal normalized by amplitude normalization and time normalization are included are compared to patterns present in both the first course and the second course.
  • This embodiment of the invention makes use of the knowledge that the event of a wheel passing past the axle counting sensor is reliably recognized as a maximum in the curve of the measurement signal. Therefore, these maxima must also occur in both measured curves of the measurement signals. If a maximum only occurs in one of the two curves of the measurement signals, the conclusion can be drawn that this is an interference signal that should not be counted per se. It is therefore advantageous to exclude this maximum from an assessment with regard to the presence of wheel spin from the outset, as a result of which incorrect identification and thus the security against errors in the identification of wheels is advantageously increased.
  • the maxima in the first profile recorded by the first axle counting sensor and the second profile recorded by the second axle counting sensor are compared and the profile of the measurement signal before and after a maximum that is to be taken into account in the dynamic time normalization , is determined taking into account a time offset between a comparable maximum of the first curve and the second curve.
  • the time offset that can be determined from these maxima can be advantageously used to obtain a speed-dependent measure for the time limits of the profile to be taken into account in the dynamic time normalization. This advantageously ensures that the course in the dynamic time normalization has a sufficient span to contain the characteristics to be assessed for a later comparison with the samples.
  • a provision device for storing and/or providing the computer program product.
  • the provision device is, for example, a storage unit that stores and/or provides the computer program product.
  • the provision device is, for example, a network service, a computer system, a server system, in particular a distributed, for example cloud-based computer system and/or virtual computer system, which stores and/or provides the computer program product preferably in the form of a data stream.
  • the provision takes place in the form of a program data block as a file, in particular as a download file, or as a data stream, in particular as a download data stream, of the computer program product.
  • this provision can also be made, for example, as a partial download consisting of several parts.
  • Such a computer program product is read into a system, for example using the provision device, so that the method according to the invention is executed on a computer.
  • the described components of the embodiments each represent individual features of the invention to be considered independently of one another, which also develop the invention independently of one another and are therefore also to be regarded as part of the invention individually or in a combination other than the one shown. Furthermore, the components described can also be combined with the features of the invention described above.
  • FIG 1 a vehicle FZ is shown which is traveling in a direction of travel FR on a track GL.
  • the vehicle FZ has bogies DG, which are each provided with two axles. These will be in figure 1 indicated by wheels RD.
  • the axle counter AZL is connected to an evaluation unit AE, which has a first computer CP1.
  • This computer CP1 is connected via a sixth interface S6 to both the first Axle counting sensor AZ1 and connected to the second axle counting sensor AZ2.
  • a single axle counting sensor AZ can also be used, which is why one of the two axle counting sensors is labeled both with the reference symbol AZ and with the reference symbol AZ1.
  • a first memory device SE1 which is connected to the first computer CP1 via a fifth interface S5, is also accommodated in the evaluation unit AE.
  • the first computer CP1 is connected to a second computer CP2 in a control center LZ via a third interface S3.
  • the second computer CP2 is also connected to a second memory device SE2 via a fourth interface S4.
  • the control center represents a facility on the track, such as an interlocking or an automatic train control system.
  • the vehicle FZ and the control center LZ have antennas AT so that they can communicate with one another via a second interface S2.
  • the vehicle FZ can communicate with a satellite STL via a first interface S1. In this way it is possible, for example, to locate the vehicle FZ, with the satellite STL being a navigation satellite.
  • the method according to the invention has program modules that can run either in the first computer CP1 or in the second computer CP2. This depends on how “intelligent” the axle counting arrangement formed by the axle counter AZL and the evaluation unit AE is.
  • FIG 2 the sequence of the method according to the invention is shown using a flowchart.
  • VL1 of the first axle counter sensor AZ1 and VL2 of the second axle counter sensor AZ2 are shown.
  • a diagram is selected for this purpose, in which the measurement signal U1, U2 is shown in the form of an output voltage over time t.
  • the subsequent processing steps of a normalization with the result of normalized curves NV1, NV2, NV3 and a comparison with patterns M1, M2 are shown.
  • the method shown is used according to the axle counter AZL figure 1 used with a first axle counting sensor AZ1 and a second axle counting sensor AZ2.
  • the figure 2 would look similar, ie the diagram of the course VL1 and the associated measures, indicated by arrows, would be omitted.
  • a normalization N is carried out according to the method according to the invention in a manner that is not detailed.
  • This normalization includes an amplitude normalization of the measurement signal U1, U2 to a target value ZW, which in the exemplary embodiment according to FIG figure 2 is at 1.
  • a dynamic time normalization is carried out, with the first profile VL1 and the second profile VL2 being considered before and after the identified maximum M1, M2, M3 to the extent that the profile associated with the maximum M1, M2, M3 can be characterized (and can be compared with patterns M1, M2, more on this below).
  • the normalized curves NV1, NV2, NV3 are created in time windows ZF1, ZF2, ZF3, as it were, which correspond to the patterns M1, M2 in terms of their temporal extension.
  • the evaluation of the first maximum M1 leads to the generation of the first standardized profile NV1 and the evaluation of the second maximum M2 leads to the generation of the third standardized profile NV3. Furthermore, a third maximum M3 can be seen both in the first curve VL1 and in the second curve VL2, which leads to the generation of a second normalized curve NV2.
  • a fourth maximum M4 can only be determined in the second profile VL2 and is therefore rejected for normalization (indicated by an X). This can be explained by the fact that the fourth maximum M4 cannot be a wheel run, since this would have to be recognizable both in the first curve VL1 and in the second curve VL2.
  • a pattern comparison of the normalized curves NV1, NV2, NV3 takes place.
  • the result here is that the first normalized curve NV1 and the third normalized curve NV3 each match the first pattern M1, which represents a wheel passage. This leads to a count of 2.
  • the second normalized course NV2 is identified using the second pattern M2, which represents a skewer. Therefore, the normalized course NV2 is excluded from a count (indicated with an X).
  • the pattern M1 and the second pattern M2 have a hatched confidence range has, which allows certain fluctuations with regard to the normalized curves NV1, NV2, NV3. This takes into account the fact that the measured curves VL1, VL2 are subject to certain tolerance fluctuations. In addition to a measurement tolerance, it must also be taken into account that different vehicles generate different measurement signals, which e.g. B. are dependent on circumstances such as the wheel wear of the vehicle.

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Mechanical Engineering (AREA)
  • Arrangements For Transmission Of Measured Signals (AREA)
  • Traffic Control Systems (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
EP21188561.1A 2021-07-29 2021-07-29 Procédé de comptage des essieux à évaluation assistée par ordinateur Active EP4124539B1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP21188561.1A EP4124539B1 (fr) 2021-07-29 2021-07-29 Procédé de comptage des essieux à évaluation assistée par ordinateur
CN202210895177.7A CN115675568A (zh) 2021-07-29 2022-07-26 一种利用计算机辅助的评估对轴计数的方法
AU2022209303A AU2022209303B2 (en) 2021-07-29 2022-07-28 Method of counting axles with computer-aided evaluation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP21188561.1A EP4124539B1 (fr) 2021-07-29 2021-07-29 Procédé de comptage des essieux à évaluation assistée par ordinateur

Publications (3)

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EP4124539A1 true EP4124539A1 (fr) 2023-02-01
EP4124539B1 EP4124539B1 (fr) 2024-05-08
EP4124539C0 EP4124539C0 (fr) 2024-05-08

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EP21188561.1A Active EP4124539B1 (fr) 2021-07-29 2021-07-29 Procédé de comptage des essieux à évaluation assistée par ordinateur

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EP (1) EP4124539B1 (fr)
CN (1) CN115675568A (fr)
AU (1) AU2022209303B2 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009030657A1 (fr) * 2007-09-03 2009-03-12 Siemens Aktiengesellschaft Procédé de comptage d'essieux sur des véhicules ferroviaires
CA2685575A1 (fr) * 2009-12-08 2011-06-08 Brian N. Southon Systeme de detection de train en voie

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009030657A1 (fr) * 2007-09-03 2009-03-12 Siemens Aktiengesellschaft Procédé de comptage d'essieux sur des véhicules ferroviaires
CA2685575A1 (fr) * 2009-12-08 2011-06-08 Brian N. Southon Systeme de detection de train en voie

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
KOLLMENT WERNER ET AL: "Towards condition monitoring of railway points: Instrumentation, measurement and signal processing", 2016 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE PROCEEDINGS, IEEE, 23 May 2016 (2016-05-23), pages 1 - 6, XP032928156, DOI: 10.1109/I2MTC.2016.7520434 *

Also Published As

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CN115675568A (zh) 2023-02-03
EP4124539B1 (fr) 2024-05-08
EP4124539C0 (fr) 2024-05-08
AU2022209303B2 (en) 2024-02-22
AU2022209303A1 (en) 2023-02-16

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