EP3053804A2 - Procede et dispositif d'optimisation d'un entretien de superstructure de voie par classification individuelle de defaut - Google Patents

Procede et dispositif d'optimisation d'un entretien de superstructure de voie par classification individuelle de defaut Download PDF

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Publication number
EP3053804A2
EP3053804A2 EP15188610.8A EP15188610A EP3053804A2 EP 3053804 A2 EP3053804 A2 EP 3053804A2 EP 15188610 A EP15188610 A EP 15188610A EP 3053804 A2 EP3053804 A2 EP 3053804A2
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EP
European Patent Office
Prior art keywords
error
track
data
single error
individual
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.)
Withdrawn
Application number
EP15188610.8A
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German (de)
English (en)
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EP3053804A3 (fr
Inventor
Christian Linder
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Deutsches Zentrum fuer Luft und Raumfahrt eV
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Deutsches Zentrum fuer Luft und Raumfahrt eV
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Publication of EP3053804A2 publication Critical patent/EP3053804A2/fr
Publication of EP3053804A3 publication Critical patent/EP3053804A3/fr
Withdrawn legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
    • B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
    • B61L23/042—Track changes detection
    • B61L23/048—Road bed changes, e.g. road bed erosion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
    • B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
    • B61L23/042—Track changes detection
    • B61L23/044—Broken rails
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
    • B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
    • B61L23/042—Track changes detection
    • B61L23/047—Track or rail movements
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
    • B61L27/53—Trackside diagnosis or maintenance, e.g. software upgrades for trackside elements or systems, e.g. trackside supervision of trackside control system conditions

Definitions

  • the present invention relates to a method for optimizing track superstructure maintenance, particularly with regard to single errors occurring in the tracks. Furthermore, the invention relates to a database used in this context.
  • track superstructure this means the structure of the rails, the sleepers, the fortifications between these two, the bedding (often gravel, concrete or steel) and the underlying underground railway infrastructure infrastructure.
  • This track superstructure like all other parts of the railway infrastructure, must be maintained, as it is exposed to operating loads and other weather conditions during operation.
  • the rails deform on sections where the rigidity of the bedding changes. This is the case, for example, for tunnel entrances and tunnel exits, because in the tunnel the sleepers and rails lie on concrete, but on the outside on gravel.
  • the gravel is due to its slightly lower rigidity under the load of the rising train something down to the concrete.
  • the track outside the tunnel slopes down slightly due to the natural setting of the ballast, but not or only imperceptibly within the tunnel because of the concrete floor. The result is a small ramp at the tunnel entrance and a sink at the tunnel exit.
  • the method according to the invention begins with the so-called initialization phase, which comprises the method steps of the identification of individual error types, the determination of reference measurements for the individual error types and the creation of the data records.
  • the use phase follows, in which first the classifier is trained with the data records created in the initialization phase, and thereafter, as soon as newly measured single error measurement values are available, they are classified by the classifier.
  • the track single fault types such as cavities, rail joints, frogs, transition zones, mud spots and hitherto unknown or unnamed individual error types are identified. This offers the advantage that more data will then be available during the later usage phase.
  • the data sets created in the next method step preferably comprise two fields.
  • the first field is an array of measurement data.
  • the individual data points of the individual errors are stored as vectors of different lengths.
  • the second field is used to store the single error type.
  • the exact individual error type of the individual error is determined directly by the classification of the measurement data of the individual error on the basis of the applied data records and assigned to the individual error. This assignment of a single fault type can be done very quickly after the measurement, without it being necessary to send a construction crew to assess the individual fault and to determine the single fault type on the track.
  • the single error type can also be determined very reliably.
  • a model is created on the basis of which a classification of later newly measured individual errors can take place.
  • the classifier can be, for example, an algorithm, a program object or a function. It is extremely important to be able to make a decision about maintenance measures related to the single error based on a reliable classification of the measurement data of the individual error and thus on a reliably determined single fault type, because different maintenance types have to be used for different types of faults. Likewise, the preparation of a forecast on the further temporal development of the individual error depends strongly on its single-fault type.
  • erroneous records can be prevented from being added to the database for known single-error types.
  • the process step of the validation can be carried out both in time before a decision on maintenance measures or an initiation of maintenance measures and in time thereafter.
  • it is particularly advantageous first to initiate construction measures, for example, and then to use the presence of a construction crew at the location of the individual fault in order to have it validated whether the assignment of the single fault type was correct.
  • the risk of incorrect allocation of resources is manageable due to the very reliable classification.
  • the subsequent inclusion of new single error types makes sense, since it can be assumed that with constant technical development of the track superstructure technology always new, previously unknown sources of error for individual errors in the track are created.
  • the new single error type is expediently validated before the creation of a new data record in order to ensure the reliability and correctness of the database of known single error types.
  • the first or the corresponding data records initially remain in the database for unknown individual error types.
  • the validation of the correctness of the classification of the measurement data of the individual error by an assessment of the individual error by one or more experts, preferably a construction crew, carried out on site.
  • This offers the advantage that, in the case of a manual assessment of the individual error, the experts, based on their experience, their specialist knowledge and the examination of the track on site, can quickly determine which source of error is involved.
  • manual validation makes it much more reliable to determine whether the classification was correct or not via computer-implemented validation procedures.
  • the known sources of error are single-fault types in which an operative connection is already known and which are often due to track-laying elements.
  • the individual faults occurring in the track include track-building elements such as switch frogs or rail joints, which produce measured data which are errors according to the current standard (if the amplitude becomes large enough). Of these elements, the position is known. Thus, reference curves of these locations can easily be determined if they are defective in the sense of the amplitude consideration, by the position of a single error identified in the measured data matches the projected position of one of these components. If this is the case, a measurement data set of a defective track-laying element is available. The same applies to sections of the route at transition zones.
  • the required reference measurements of elements where the position is known can be cut from the position based on existing vertical track deviation measurements along the track.
  • the positions of the switch frogs can be read out using the switch directory. If you now isolate the measured values at these positions with an epsilon of, for example, 15 meters to the right and left of the switch frog piece, you will get very comfortable and uncomplicated reference measurements for switch frogs.
  • the choice of an appropriate Epsilons is advantageous in this case, since it can ensure that one selects and stores a maximum information content of the single error curve. Similarly, you can proceed with all other track construction elements (eg tunnel entrances and tunnel exits).
  • a further embodiment of the invention is that the measurements are measurements of the vertical deflection of the tracks from their desired position as a function of the line kilometers, preferably under load of the tracks by a measuring vehicle driving thereon, and in the case of the single error by corresponding vertical single errors is.
  • the possible fault patterns in the track superstructure are manifold.
  • vertical individual errors of these varied fault patterns occur particularly frequently and, due to their physical orientation, have a particularly noticeable influence on the vehicle reaction of a rail vehicle and thus on ride comfort and driving safety.
  • methods of data mining, methods of multivariate data analysis, methods of classification and of supervised and unsupervised learning, such as decision trees, neural networks, support vector are used to train the classifier and / or to classify the measurement data of the individual error Machines, Genetic Algorithms, as well as k-nearest-neighbors, kNN, algorithms applied.
  • the previously mentioned methods make it possible to train the classifier better with each new reference measurement, and thus also the quality of the results of the classification carried out by the classifier constantly improves with time.
  • the aforementioned methods are only to be seen as examples.
  • a further embodiment of the invention is that regression methods and / or within the framework of the classification of the measurement data of the individual error Correlation method used to assess the degree of similarity or correspondence between the single error measurement curve to be classified and the reference measurement curves. Depending on the order of the respective regression method, a very good functional description of the measurement data of the individual error can be achieved. If the result of the regression method is included in the further evaluation of the individual error, one can speak of an extensive consideration of the single-error form.
  • correlation methods is also particularly advantageous, as these enable the relationship between the measurement curve to be classified and the reference measurement curves to be illuminated.
  • a further embodiment of the invention is that one or more features of the measurement curve of the individual error, such as amplitudes, frequencies, slopes, integrals, or the entire shape of the measurement curve of the individual error is taken into account within the classification of the measurement data of the individual error.
  • the probability of a correct classification of the individual error can be increased with an increasing number of considered features. It is therefore particularly advantageous to compare the measurement curve of the individual error with the respective reference measurement curves as holistically as possible.
  • which features are taken into account and to what extent depends to a large extent on the algorithm used.
  • each individual fault type must be maintained differently.
  • the ballast In hollow layers, for example, the ballast must be stuffed, including a hand tamping machine or even a stuffing box is necessary, which compresses the underlying ballast again.
  • the steel In the case of center piece individual defects, the steel is ground into the appropriate shape, for which corresponding grinding machines are required.
  • specially trained experts such as switch mechanics or point masters must be present when working on switches.
  • the information about the single fault type to be expected also allows a better scheduling of track closures, whereby a loss of availability of the route can be kept low. This is particularly the case because it is known how long it takes to repair a single fault of a particular type. In connection with the requirement of track closures, it must be taken into account in particular that construction crews in the track must be operationally secured due to occupational safety. This means that in a maintenance measure, depending on the size of the process, neighboring tracks must be locked or slow driving must be set up. If now too large machines are ordered due to ignorance of the exact single fault type, the corresponding safeguards can also be too large, which leads to loss of availability and the generation of delay minutes. All these problems do not occur in the present process.
  • Another advantage is that knowledge of the single-fault type and the possible error-type-specific prognosis of the expected degradation behavior of the individual error no longer limits one to a linear regression model in the prognosis.
  • Such a linear relationship has hitherto often been achieved by merely looking at the course of the amplitudes of the same single error over a few consecutive days. So far, this has been very inaccurate, since different types of single defects can also differ in their degradation behavior.
  • a faulty rail joint can also develop logarithmically over time, as it is driven off the trains over time.
  • a hollow layer is getting worse and destroys the adjacent gravel under operating load, where This single error can also behave exponentially.
  • Such error-type-specific forecasts of the development of individual errors thus offer the advantage that, for example, with two different individual error types, the decision can be made to eliminate one single-fault type due to its probable development over elimination of the other single-fault type despite the same single error amplitude of the two individual errors.
  • Such an assessment of how long it will be possible to delay the elimination of a single fault is particularly advantageous with regard to minimizing the timetable failures and with regard to the annual financial budget of the railway undertakings.
  • the decision on maintenance measures may also include considerations and decisions with regard to ride comfort and passenger safety.
  • the present method also allows a maintenance manager to be informed early on the exact single fault type. Maintenance managers must make decisions based on certain information, such as when, where, and how a track track is renewed or maintained. Through the present process, we will expand the information available and optimize the decision-making process of the maintenance manager. For example, the maintenance manager can optimally dimension the construction safety at an early stage. This results in big positive effects in terms of cost savings during the maintenance process.
  • the object of the present invention is achieved not only by the method described above, but also by a database used in this context.
  • the database for optimizing the track superstructure maintenance according to the invention comprises data sets that each consist of reference error measurement values of a single error that occurs in the track and emerges in track surveying, in the form of a measured data vector and an associated single error type, with the data records a classifier is trained to classify later using the data sets new measurement data of a single error and being at least partially based on this classification on maintenance measures based on the single error.
  • the systematic construction of the database with reference error measurement values and associated single error type together with a possibility to constantly expand the database by adding new data sets enables a continuous improvement of the training of the classifier and thus an increasingly reliable determination of single error types.
  • This is related to the increasing quantity of original measurement data from reference measurements taken into account in the training and classification procedures.
  • the database is a database for known single-error types.
  • a corresponding database for unknown individual error types has similar data records with reference error measurement values in the form of a measurement data vector whose individual error type, however, initially remains indefinite.
  • a method for optimizing track superstructure maintenance is provided by means of the method described above, in which the single-fault type can be determined on the basis of already existing individual-error-original measured data for each newly measured individual error. Furthermore, a database that can be used in this context will be provided for the first time.
  • the FIG. 1 shows an exemplary measurement result of the vertical track deflection in mm along a measured track distance, that is, depending on the worn track kilometer of the track section. Looking at the course of the trace, you will find some significant increases and decreases in the vertical track deflection. For a large number of these increases and decreases, an operative connection, that is to say the respective type, the respective cause or the respective source of error, is known. Viewed from the left, the first increase W occurs due to a known position of a switch frog, the second increase B and the first decrease T due to the known positions of tunnel entrances and exits and the last two increases Ü (or combinations of increase and decrease) due to a known position of a railroad crossing.
  • the amplitude limit G is ⁇ 11 mm.
  • the measurement data of these individual errors must now be cut out in order to be examined more closely. Noticeable here is above all the third increase Z seen from the left, since this is a single fault on a free route with an unknown single fault type. Since its cause is not known, an assessment must be made accordingly.
  • the FIG. 2 shows an exemplary listing of possible records in a database for known single error types.
  • the data points of the individual errors are stored as vectors of different lengths.
  • the second field the individual error types are stored, whereas in the right column the data records are numbered consecutively for the sake of clarity.
  • the single fault types listed here (tunnel, railway crossing, switch frog) are purely exemplary.
  • the FIG. 3 shows a first embodiment of the method according to the invention.
  • the method begins with the identification of a single-fault type by a classical amplitude analysis (step 1).
  • step 2 the measuring range relevant for the single error is extracted from the overall measurement.
  • step 3 the extracted measurement range of the single error is classified based on the data records in the database (step 3.).
  • this embodiment is the single error type B.
  • step 4 the correctness of the classification is validated (step 4). Since in this embodiment the classification was validated as correct, in the last step (step 5) a corresponding new data record is created in the database for known single error types.
  • the FIG. 4 shows a second embodiment of the method according to the invention.
  • an initialization phase has already taken place and thus already a database 1 for known single error types is available.
  • the procedure begins with a test drive (see step I.) in which track deflections along the test section are detected. Subsequently, single errors from the total measurement (see step II.). In a next step, the measurement data of the individual errors are classified on the basis of the data records in the database 1 (see step III.). Subsequently, the correctness of the classification is validated (see step IV.). If the classification is validated as correct, a new record or records will be created in database 1 (see step V.).
  • step VI. Occurs to identify new single-fault types.
  • each x represents a reclassified single error n to an n-dimensional feature space.
  • a cluster algorithm is applied to the unclassified single errors for the defined number of clusters (see step VI.1.).
  • the quality of the result of the applied cluster algorithm with quality measures such as the inner-class homogeneity and the inter-class heterogeneity is determined (see step VI.2.).
  • the determined quality now yields an optimal number of 3 classes, which in turn indicates that 3 are still unknown Single error types exist (see step VI.3.).
  • the new error type is validated in the field before it is added to the database 1 (see step VI.4.).
  • FIG. 5 shows a schematic overview of the embodiments of the Figures 3 and 4 with further aspects of the method according to the invention.
  • the schematic overview begins with an initialization phase 501 identified within the individual error types and corresponding reference measurements are determined.
  • a conclusion of the initialization phase is the creation of data records in a database 502 with reference measurement data for single errors whose type is known.
  • the identification of individual errors and the extraction of measured values of the individual errors from a total measuring data set of a measuring train has already been described in more detail in FIGS. 3 and 4.
  • a classifier 505 classifies the measurement data of each individual error based on the data records in the database 502.
  • an optimal repair of the single error is then initiated (see 506). Subsequently, the correctness of the classification is validated (see 507). If the classification is correct, another reference record is added to the database 502 (see 508) to further improve the classification for the future. If the classification was incorrect, a reference record is also added (see 509), but to a database 510 with single measure reference metrics whose type is not known. In a next step, the attempt is made by means of a clustering method, which in connection with the FIG. 4 to find new types of errors (see 511).
  • a corresponding single error is either added to the single error reference measurement database 502 whose type is known (see 513) or it remains in the database 510 with reference measurement data for single errors whose type is not known (see 514).

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Machines For Laying And Maintaining Railways (AREA)
EP15188610.8A 2014-12-18 2015-10-06 Procede et dispositif d'optimisation d'un entretien de superstructure de voie par classification individuelle de defaut Withdrawn EP3053804A3 (fr)

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DE102014119095.5A DE102014119095A1 (de) 2014-12-18 2014-12-18 Verfahren und Einrichtung zur Optimierung der Gleisoberbauinstandhaltung durch Einzelfehlerklassifikation

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EP3053804A3 EP3053804A3 (fr) 2016-10-05

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ES2877231T3 (es) * 2016-12-08 2021-11-16 Siemens Rail Automation S A U Sistema y método de inspección de railes
FR3084046B1 (fr) * 2018-07-17 2022-04-22 Alstom Transp Tech Procede de surveillance de l'etat d'une ligne aerienne de contact et dispositif programme associe
DE102018219256A1 (de) * 2018-11-12 2020-05-14 Siemens Mobility GmbH Ermitteln einer Degradation einer bestimmten Gleiskomponente
DE102020118670A1 (de) 2020-07-15 2022-01-20 Deutsche Bahn Aktiengesellschaft Beobachtungsverfahren
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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EP1271364A2 (fr) 2001-06-20 2003-01-02 Erdmann-Softwaregesellschaft mbH Méthode pour simuler l'état des routes de transport
KR20110134547A (ko) 2010-06-09 2011-12-15 한국철도기술연구원 가속도계를 이용한 궤도 틀림 검측 시스템 및 검측 방법
DE102011101226A1 (de) 2011-05-11 2012-11-15 Deutsche Bahn Ag Verfahren zur Beschreibung von Gleislageabweichungen
EP1977950B1 (fr) 2007-04-03 2013-04-10 DB Netz Aktiengesellschaft Procédé destiné à l'évaluation en fonction de l'effet de la qualité de stockage d'une voie

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US20050279240A1 (en) * 2004-06-22 2005-12-22 Pedanekar Niranjan R Enhanced method and apparatus for deducing a correct rail weight for use in rail wear analysis of worn railroad rails
DE102004045457B4 (de) * 2004-09-20 2009-04-23 Deutsche Bahn Ag Verfahren zur Diagnose und zum Zustandsmonitoring von Weichen, Kreuzungen oder Kreuzungsweichen sowie Schienenstößen durch ein Schienenfahrzeug
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EP1271364A2 (fr) 2001-06-20 2003-01-02 Erdmann-Softwaregesellschaft mbH Méthode pour simuler l'état des routes de transport
EP1977950B1 (fr) 2007-04-03 2013-04-10 DB Netz Aktiengesellschaft Procédé destiné à l'évaluation en fonction de l'effet de la qualité de stockage d'une voie
KR20110134547A (ko) 2010-06-09 2011-12-15 한국철도기술연구원 가속도계를 이용한 궤도 틀림 검측 시스템 및 검측 방법
DE102011101226A1 (de) 2011-05-11 2012-11-15 Deutsche Bahn Ag Verfahren zur Beschreibung von Gleislageabweichungen

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EP3053804A3 (fr) 2016-10-05

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Effective date: 20170406