Method for detecting damage to a transport system and control device therefor

Abstract

The proposal relates to a method for detecting damage to a transport system (100), which comprises a rail-based vehicle (10) and an infrastructure element (20) that can be passed by the rail-based vehicle (10), by means of a plurality of sensors (12, 14, 16, 18) arranged on the rail-based vehicle (10). The plurality of sensors (12, 14, 16, 18) can be operated in a first measurement state and a second measurement state. The method comprises a first measurement data acquisition step (Sa1) for acquiring first measurement data by way of at least one sensor (12, 14, 16, 18) operated in the first measurement state and a second measurement data acquisition step (Sa2) for acquiring second measurement data from the sensors (12, 14, 16, 18) operated in the second measurement state. Furthermore, the method comprises a first match determination step (Sb1) for determining a first match between the first measurement data and a first stored comparison dataset (32) and a second match determination step (Sb2) for determining a second match between the second measurement data and a second stored comparison dataset (42). Moreover, the method comprises a first damage detection step (Sc1) for detecting damage to the rail-based vehicle (10) on the basis of the first match and a second damage detection step (Sc2) for detecting damage to the infrastructure element (20) that can be passed by the rail-based vehicle (10) on the basis of the second match. A control device (70) for carrying out the method is also proposed.

Description

Technical area

[0001] The present invention relates to a method for detecting damage to a transport system comprising a rail-bound vehicle and an infrastructure element traversable by the rail-bound vehicle. The invention also relates to a control device for implementing the method. State of the art

[0002] Document US 10,953,900 B2 discloses an abnormality detection device in which a plurality of vehicles moving on a rail are each equipped with an acceleration sensor. Each of the vehicles is equipped with an acceleration sensor, and the acceleration data from all sensors is evaluated to detect an abnormality in the vehicle or on the rails.

[0003] Document EP2432669B1 discloses a device for monitoring a rail vehicle with wheels using acceleration sensors. If deviations from the sinusoidal shape occur periodically with the axle frequency at the same axle angle, this indicates malfunctions. Rail damage can be detected by recording singular acceleration values in the vertical direction. Description of the invention

[0004] The invention relates, in one aspect, to a method for detecting damage to a transport system comprising a rail-bound vehicle and an infrastructure element passable by the rail-bound vehicle, according to method claim 1.

[0005] The rail-bound vehicle can be a train, for example a passenger train or a freight train. In this case, the infrastructure element that the rail-bound vehicle can pass through can be a track. The track can comprise a track bed, a rail, a railway sleeper and corresponding fastening elements therefor. Alternatively, the transport system can be a cable car. In this example, the rail-bound vehicle can be the gondola of a cable car and the infrastructure element can be a funicular of the cable car or a guide rail of the funicular. In another example, the rail-bound vehicle can be a tram and the infrastructure element can be a tram rail.Damage to the transport system may be damage to the rail-bound vehicle or damage to the infrastructure element that the rail-bound vehicle can pass through.

[0006] The method is carried out using a plurality of sensors arranged on the rail-bound vehicle. The plurality of sensors can be operated in a first measuring state and a second measuring state. The sensors can be operated independently of one another. Accordingly, each sensor can collect individual measurement data and forward it to a higher-level evaluation unit. Alternatively, the sensors can also be calibrated to one another. In this case, the sensors collect measurement data depending on the measurement data collected by the other sensors. This measurement data can then be aggregated and subsequently forwarded to the higher-level evaluation unit. Depending on the measurement state, the sensors can record different types of measurement data. The individual measurement parameters of the first and second measurement states can be different.

[0007] The method comprises a first measurement data acquisition step for acquiring first measurement data by at least one sensor operated in the first measurement state and a second measurement data acquisition step for acquiring second measurement data from the sensors operated in the second measurement state. According to the method, the first measurement data can be sufficiently acquired by a sensor operated in the first measurement state. In contrast, the second measurement data can be acquired by all sensors operated in the second measurement state. The first and second measurement data can be determined depending on the type of sensor. For example, the sensors can be acceleration sensors. The first and second measurement data can then be acceleration data. Alternatively, the sensors can be force sensors for acquiring a force acting on the sensors.The first and second measurement data can be detected forces. Other types of sensors, such as inclination sensors or optical sensors, can also be used according to the first aspect of the invention.

[0008] The first and second measurement states of the sensors can be adapted to the respective parameter to be measured. For example, with an optical sensor, two different frequency ranges of the electromagnetic spectrum can be recorded in the first and second measurement states, respectively. If the sensors are inclination sensors, the inclination can be recorded in the first and second measurement states in relation to different coordinate systems. Alternatively or additionally, the parameter to be measured can be recorded with different levels of accuracy in the first and second measurement states of the sensors, respectively. The first and second measurement data can either be recorded directly and further processed according to the method. Alternatively, the first and second measurement data can be recorded first and then preprocessed in a subsequent step.In other words, a directly measured parameter can be converted into a parameter for evaluation. Preprocessing can involve, for example, Fast Fourier analysis, wavelet analysis, order analysis, or principal component analysis.

[0009] The method further comprises a first match determination step for determining a first match between the first measurement data and a first stored comparison data set, and a second match determination step for determining a second match between the second measurement data and a second stored comparison data set. To generate the first comparison data set, for example, an already damaged rail-bound vehicle can be measured using the at least one sensor operated in the first measurement state. The data collected in this process can then form the first comparison data set. Alternatively or additionally, the first comparison data set can be created as part of a prepared measurement. In this case, a rail-bound vehicle can have been prepared according to a type of damage to be detected using the method.The data collected during the measurement of the prepared rail-bound vehicle with a sensor operated in the first measurement state can then form the first comparison data set. The second comparison data set can, for example, have been created during a prepared comparison run. During such a comparison run, an infrastructure element of a transport system can have been prepared according to a damage to be detected by the method. The measurement data collected by the sensors operated in the second measurement state while the prepared infrastructure element passes can form the second measurement data set. Alternatively or additionally, an infrastructure element that was already damaged previously can be measured using the sensors in the second measurement state. The measurement data collected during the measurement of the previously damaged infrastructure element can form the second comparison data set.To determine the first match or the second match, a partial match of the first measurement data with the first comparison data set or a partial match of the second measurement data with the second comparison data set may be sufficient. In other words, a complete match of the measurement data with the respective comparison data set is not necessary to determine a match.

[0010] The method further comprises a first damage detection step for detecting damage to the rail-bound vehicle, depending on the first match, and a second damage detection step for detecting damage to the infrastructure element passable by the rail-bound vehicle, depending on the second match. Damage to the rail-bound vehicle can, for example, be damage in a wheel area of the rail-bound vehicle. In particular, it can be damage to a wheel bearing or a wheel of the rail-bound vehicle. However, damage to other areas of the rail-bound vehicle can also be detected. If the rail-bound vehicle is a train with several connected wagons, damage to a connecting element of the individual wagons can also be detected.Alternatively or additionally, damage to a frame element or a housing of the rail-bound vehicle can also be detected. Damage to the infrastructure element passable by the rail-bound vehicle can, for example, be damage to a rail or a wheel-receiving element for receiving a wheel of the rail-bound vehicle in the transport system. If the rail-bound vehicle is a cable car, damage to the cable car's funicular can also be detected, for example. If the rail-bound vehicle is a train, damage to a track bed and / or a belt rail can also be detected. Further damage to the rail-bound vehicle or to an infrastructure element of the transport system passable by it can also be detected by the method according to the first aspect.As already mentioned above, depending on the comparison chosen, a partial agreement of the first or second measurement data with the first or second comparison data set may be sufficient to detect damage.

[0011] The proposed method for detecting damage to a transport system thus makes it possible, on the one hand, to detect damage to an infrastructure element of the transport system using just a few sensors. In particular, not all vehicles moving in the transport system need to be equipped with sensors. Furthermore, not all sensors need to be queried to obtain the second measurement data. Finally, not all of the obtained measurement data need to be above a specified threshold. To detect damage to an infrastructure element, even a partial match between the second measurement data and the stored comparison data set is sufficient. The proposed method thus enables faster and simpler detection of damage to a transport system.

[0012] According to one embodiment of the method, the sensors arranged on the rail-bound vehicle can be configured to acoustically detect an acceleration of the rail-bound vehicle relative to the infrastructure element traversed by the rail-bound vehicle using predeterminable detection frequencies. The detection frequencies can, in particular, be specified externally, for example, by a higher-level control device, which can specify a detection frequency of the sensors using control signals. Alternatively, the sensors can also specify a specific detection frequency independently of one another or independently of a higher-level control device. For example, if a specific condition is met, a sensor can switch from a first detection frequency to a second detection frequency. The sensors can therefore be adapted to different operating conditions within the transport system.Furthermore, the recording and evaluation of acoustic signals represents a particularly simple method for determining acceleration. Acoustic acceleration sensors are also generally readily available, so the process can be carried out using simple and cost-effective means.

[0013] According to a further embodiment, a first subset of the sensors can be arranged at a front end section of the rail-bound vehicle in the direction of travel, and a second subset of the sensors can be arranged at a rear end section of the rail-bound vehicle in the direction of travel. The first and the second subset can each comprise at least two sensors. The front end section can be arranged on a traction vehicle, such as a locomotive, of the rail-bound vehicle. In particular, the front end section can limit the rail-bound vehicle to the front in the direction of travel. If the rail-bound vehicle is the gondola of a cable car, the front end section can be arranged at the front of the gondola in the direction of travel.The rear end section can be the rear end of the last carriage in the direction of travel of a train. If the rail-bound vehicle is the gondola of a cable car, the rear end section can be located at the rear end of the gondola in the direction of travel.

[0014] The at least two sensors can be arranged symmetrically on the front or rear end section of the rail-bound vehicle. For example, one of the sensors can be arranged on an outer section of the front or rear end section that is on the left in the direction of travel of the rail-bound vehicle. A second of the sensors can then be arranged on an outer section of the front or rear end section that is on the right in the direction of travel of the rail-bound vehicle. Alternatively or additionally, at least one sensor can be arranged on an outer section of the front or rear end section of the rail-bound vehicle that is upper in the direction of travel of the rail-bound vehicle. Another of the sensors can be arranged on an outer section of the front or rear end section of the rail-bound vehicle that is lower in the direction of travel of the rail-bound vehicle.The arrangement of sensors at a front and rear end section of the rail-bound vehicle has the advantage that acceleration data can be recorded without interference from other components of the rail-bound vehicle located between the front and rear end sections. The use of at least two sensors at the front and rear end sections enables redundant recording of acceleration data at the respective end sections.

[0015] According to a further embodiment, the method can comprise a first measuring step for operating the first subset of sensors in the first measuring state and a second measuring step for operating the second subset of sensors in the second measuring state. The first measuring step and the second measuring step can be carried out simultaneously. For example, the sensors arranged at the front end section of the rail-bound vehicle can be operated in the first measuring state, while at the same time the sensors arranged at the rear end section of the rail-bound vehicle can be operated in the second measuring state. The first and second measuring states can be set by specifying different detection frequencies for the respective sensors. The simultaneous execution of the first and second measuring steps enables the simultaneous detection of first and second measured values.This can shorten the duration of the procedure.

[0016] In this embodiment, the method can comprise a first changeover step for changing the measurement state of the first subset of sensors from the first to the second measurement state when a predetermined condition is met. Furthermore, the method can comprise a second changeover step for changing the measurement state of the second subset of sensors from the second to the first measurement state when the predetermined condition is met. For example, the predetermined condition can be the achievement of a predetermined measurement duration of the sensors in the first or second measurement state. Alternatively or additionally, the predetermined condition can be the detection of a standstill of the rail-bound vehicle, for example by detecting a negative acceleration followed by a long-lasting zero acceleration.Alternatively or additionally, the predetermined condition can also be the detection of a maximum acceleration, above which the sensors can no longer detect any further acceleration in the selected measurement state. By changing the measurement state when a predetermined condition is met, the measurement method can be adapted to different operating situations of the transport system. This allows the detection of measurement data recorded in a faulty operating state of the transport system to be avoided and / or corrected by changing the measurement state.

[0017] According to the invention, the first measurement data acquisition step comprises specifying a first acquisition frequency of the sensors and acquiring the first measurement data at the specified first acquisition frequency and a specified first acquisition time. The first acquisition frequency and the first acquisition time can be adapted to the acceleration data acquired in the first measurement state. For example, the first acquisition frequency can have a value greater than 1500 Hz, in particular 1660 Hz. This high-frequency sampling rate is particularly suitable for detecting damage to the rail-bound vehicle. In this example, the first acquisition time can be more than 10 seconds, in particular 12 seconds per sensor. Due to the high sampling rate, this acquisition time is sufficient to acquire the first acceleration data.At the same time, the higher power consumption of the sensors operated in the first measurement state, which is associated with the high sampling rate, is reduced by choosing a correspondingly shorter acquisition time.

[0018] According to the invention, the second measurement data acquisition step comprises specifying a second acquisition frequency of the sensors and acquiring the second measurement data at the specified second acquisition frequency and a specified second acquisition time. The first acquisition frequency and the second acquisition frequency differ in this case. The first acquisition time and the second acquisition time also differ in this case. For example, the second acquisition frequency can be less than 100 Hz, in particular less than 50 Hz. This low-frequency sampling rate is particularly suitable for detecting damage to an infrastructure element passable by the rail-bound vehicle. The second acquisition time can be less than 6 hours, in particular 4 hours.Due to the lower power consumption of the sensors operating in the second measurement state associated with the low-frequency sampling rate, the second acquisition time can be significantly increased to adequately capture the second measurement data. The sensors operating in the first and second measurement states can therefore be adapted to the type of first and second measurement data, respectively. At the same time, the energy consumption of the sensors required to acquire the measurement data can be regulated.

[0019] According to a further embodiment, the method can comprise a first classification step for classifying the first measurement data into at least two classes depending on the first match. Furthermore, the method can comprise a second classification step for classifying the second measurement data into at least two classes depending on the second match. The two classes can be identical for the first and second measurement data. Alternatively, the first and second measurement data can each be classified into two different classes. The two classes can describe different types of damage, e.g., damage to the rail-bound vehicle or damage to an infrastructure element passable by the rail-bound vehicle. For example, the first and second measurement data, respectively, can be classified into a "defective" and a "good" class.For example, only those measurement data for which the detection of damage is sufficiently ruled out are classified as "OK." In contrast, all measurement data that indicate even slight damage are classified as "defective." Dividing the first and second measurement data into at least two classes allows for the quantification of known damage. This can increase the accuracy of damage detection.

[0020] In this embodiment, the at least two classes can be differentiated based on a graded damage category. The graded damage category allows the severity of the damage to be classified more precisely. For example, the classification into the classes can be "okay," "minor damage," "severe damage," and "very severe damage." Depending on the classification of the measurement data into one of the damage classes, a person responsible for the transport system, for example, a railway operator, can decide whether a particular section of the transport system needs to be repaired immediately or whether routine maintenance may need to be performed at a later date. Accordingly, the user's needs can be taken into account when implementing the method. This can increase user-friendliness.

[0021] In one of the embodiments described above, the first class classification step can comprise comparing the first measurement data with a subset of the first comparison data set. Furthermore, the second class classification step can comprise comparing the second measurement data with a subset of the second comparison data set. For example, the subset of the first or second comparison data set can be selected based on a distance measure. For example, the first measurement data and the first comparison data set can be graphically represented as a two- or three-dimensional point set. A geometric distance can then be determined between the individual data points of the graphical representation of the first measurement data and the individual data points of the graphical representation of the first comparison data set. The subset can then be selected based on this distance.Depending on the classes into which the individual data points of the first measurement data were classified, the entirety of the first measurement data can also be classified into one of at least two classes.

[0022] The method described in connection with the first measurement data for classifying the first measurement data into at least two classes can also be applied analogously for classifying the second measurement data into the two classes based on a comparison of the second measurement data with a subset of the second measurement data set.

[0023] Classifying the first or second measurement data into at least two classes by comparing it with a subset of the first or second comparison data set offers the advantage that only a subset of the comparison data set is considered. This requires fewer comparison steps than a comparison with the entire comparison data set. The computational effort and the number of calculations required to perform the comparison can thus be minimized.

[0024] In this embodiment, the subset of the first or second comparison data set can comprise a plurality of data points, and the comparison can be performed using the plurality of data points. As already described above, the respective data points can be selected, for example, based on a distance measure. Depending on the type of measurement data, a subset suitable for performing the comparison can be selected with regard to specific criteria by selecting suitable data points from the respective comparison data sets. The proposed method for damage detection can thus be adapted to different operating states of the transport system. Furthermore, the proposed method for damage detection can be adapted to various user specifications.

[0025] According to a further embodiment of the method, the first measurement data acquisition step, the first consistency determination step, and the first classification step can be repeated multiple times within a predetermined period of time. Furthermore, the first damage detection step can comprise detecting damage to the rail-bound vehicle if a majority of the first measurement data acquired within the predetermined period of time has been classified into a class that corresponds to damage to the rail-bound vehicle based on the graded damage category. The first measurement data acquisition step, the first consistency determination step, and the first classification step can be repeated regularly, for example, in particular five times per day. This allows the status of the transport system to be recorded at different times within a day.The actual loads on the transport system during the day can thus be reproduced more accurately.

[0026] For example, damage to the rail vehicle is only detected after five repetitions of the first measurement data acquisition step, the first match determination step, and the first class classification step, with three of the five initial measurement data being classified into the class corresponding to damage to the rail vehicle. This allows randomly occurring measurement errors to be compensated for and the accuracy of damage detection to be improved.

[0027] According to a further embodiment of the method, the second measurement data acquisition step, the second consistency determination step, and the second class classification step can be repeated multiple times within a predetermined period of time. Furthermore, the second measurement data can comprise a plurality of data points, and the second damage detection step can comprise detecting damage to an infrastructure element passable by the rail-bound vehicle if a predetermined proportion of the data points of the second measurement data acquired within the predetermined period of time have been classified into a class that, based on the graded damage category, corresponds to damage to an infrastructure element passable by the rail-bound vehicle.For example, damage to an infrastructure element can only be detected if two of the three data points in the second measurement data set are classified in the class corresponding to damage to the infrastructure element. This can compensate for randomly occurring measurement errors and improve the accuracy of damage detection.

[0028] In this embodiment, the predetermined number of data points of the second measurement data acquired within the predetermined period of time can be greater than 50%, in particular greater than 90%, of the total data points of the second measurement data acquired within the predetermined period of time. In particular, the predetermined number can be less than 100% of the total data points of the second measurement data acquired within the predetermined period of time. Accordingly, in order to detect damage to an infrastructure element, complete agreement between the second measurement data and the second comparison data set is not necessary. During the acoustic acquisition of acceleration data, it can happen that acceleration data that could indicate damage is not acquired, for example due to loud noises or inappropriate selection of the sampling times.If a complete match between the second measurement data and the second comparison data set is required to detect damage, part of the second measurement data cannot be used to detect damage due to these interferences. If, on the other hand, only a partial match, in particular a 90% match between the second measurement data and the second comparison data set, is required to detect damage, the occurrence of the short-term interferences described above plays a lesser role. Although the second measurement data recorded during the occurrence of the interference cannot be used to detect damage, damage to the infrastructure element can still be reliably detected based on the remaining second measurement data. The accuracy of the method for detecting damage to an infrastructure element can thereby be increased.

[0029] In a further aspect, the invention relates to a control device comprising a communication interface for receiving measurement data as described above. The control device is configured to carry out the method according to the first aspect. For an understanding of the individual features and their advantages, reference is made to the above explanations. Short description of the drawings

[0030] Figure 1 schematically shows a transport system with sensors operated in different measuring states according to an embodiment of the invention. Figure 2 schematically shows the transport system of the Figure 1 according to a further embodiment of the invention. Figure 3 shows a flowchart with steps of a method for detecting damage to a transport system according to an embodiment of the invention. Figure 4 shows a flowchart with reference to the Figure 3 shown procedures applicable steps. Detailed description of embodiments

[0031] Figure 1 shows schematically a transport system 100 with a rail-bound vehicle 10 and an infrastructure element 20 that can be passed by the rail-bound vehicle 10. The transport system 100 is in Figure 1 depicted in the form of a railway line on which a train 10 moves along a railway track 20. The train 10 comprises a plurality of carriages 10a, 10b, 10c. The railway track 20 comprises a plurality of railway sleepers 22, two rail strands 24, and a track bed 26. A plurality of sensors 12, 14, 16, 18 are arranged on the train 10 and are designed to acoustically detect an acceleration of the train 10 relative to the railway track 20 at predeterminable detection frequencies. A first subset 12, 14 of the sensors is arranged at a front end section 11 of the train 10. A second subset 16, 18 of the sensors is arranged at a rear end section 13 of the train 10.

[0032] In the Figure 1In the illustrated embodiment, the first subset 12, 14 of the sensors is operated in a first measuring state. The second subset 16, 18 of the sensors is operated in the embodiment of the Figure 1 operated in a second measuring state. In the first measuring state, the sensors 12, 14 record acceleration values of the train 10 relative to the railway track 20 at a first recording frequency of 1660 Hz. In other words, the sensors 12, 14 are in a high-frequency measuring state, in Figure 1represented by HF. The acceleration values recorded by the sensors 12, 14 are processed, for example by means of electronic signal processing, into first acceleration data, which are representative of an acceleration of the train 10 relative to the railway track 20. Furthermore, the second subset 16, 18 of the sensors with a second recording frequency of less than 50 Hz also records acceleration values of the train 10 relative to the railway track 20. The low-frequency second measurement state of the sensors 16, 18 is in the Figure 1represented by NF. The acceleration values detected by the sensors 16, 18 are processed, for example by means of electronic signal processing, into second acceleration data, which are representative of an acceleration of the train 10 relative to the railway track 20. By means of the sensors 12, 14 operated in the high-frequency first measurement state HF, damage 50 on the rail-bound vehicle 10 can be detected. In the Figure 1 The damage 50 is shown in the form of a vibration of the wagon 10a relative to the railway track 20, indicated by two double arrows. The vibration 50 can be caused, for example, by damage to a wheel and / or a wheel bearing of the wagon 10a. Furthermore, the sensors 16, 18 operated in the low-frequency measurement state NF can detect damage 60 to the infrastructure element 20. The damage 60 to the infrastructure element 20 is shown in the Figure 1in the form of a fracture point 60 on the railway sleepers 22 and the rail track 24.

[0033] The acceleration data acquired by sensors 12, 14, 16, 18 are transmitted to a communication interface 72 of a control device 70. The acceleration data generated by sensors 12, 14 operating in the first measurement state (HF) is acquired as first measurement data 30 by the control device 70. The acceleration data generated by sensors 16, 18 operating in the second measurement state (LF) is acquired as second measurement data 40 by the control device 70. The control device 70 compares the first measurement data 30 with a first comparison data set 32 to determine a first match according to the procedure described above. Such a comparison of the first measurement data 30 with the first comparison data set 32 is described below.

[0034] In one example, the first measurement data 30 and the first comparison data set 32 are graphically represented as a two- or three-dimensional point set. For example, the first measurement data 30 and the first comparison data set 42 can be represented as a point set in the form of a two-dimensional graph. Alternatively, the first measurement data 30 and the first comparison data set 32 can be represented as a point set in the form of a three-dimensional grid. A distance measure, such as the Euclidean distance, can then be applied between the graphical representation of the first measurement data 30 and the graphical representation of the first comparison data set 32. The Euclidean distance here refers to the length of the shortest connecting line between two points arranged in space or in a plane. This distance is invariant under movement.This distance measure is then applied between all points of the point set of the graphical representation of the comparison data set 32 and each individual point of the point set of the graphical representation of the first measurement data 30. Now, for each point of the point set of the graphical representation of the first measurement data 30, the k data points of the comparison data set 32 are selected that have the smallest distance measure to the respective point of the first measurement data 30. These k data points of the first comparison data set 32 can then form the subset of the first comparison data set 32. Advantageously, k=3. Subsequently, a check is performed to determine the classes into which the k data points of the subset have been assigned.If a majority of the k data points of the subset of the first comparison data set 32 have been classified into a specific class, the respective point of the point set of the graphical representation of the first measurement data 30 can also be classified into this class. For example, if a majority of the points of the subset of the comparison data set 32 were classified into the damage class "OK," the point in question from the point set of the first measurement data 30 can also be classified into the damage class "OK." If, however, a majority of the points of the subset of the comparison data set 32 were classified into the damage class "defective," the point in question from the point set of the first measurement data 30 can also be classified into the damage class "defective." This procedure can be repeated for all points of the point set of the graphical representation of the first measurement data 30.

[0035] The control device 70 also compares the second measurement data 40 with a second comparison data set 42 to determine a second match according to the procedure described above.

[0036] Depending on the first match, the control device 70 detects the damage 50 on the rail-bound vehicle 10 according to the procedure described above. Depending on the second match, the control device 70 detects the damage 60 on the infrastructure element 20 according to the procedure described above.

[0037] Figure 2 shows schematically the transport system 100 according to Figure 1 at a time when a first changeover step or a second changeover step for changing the respective measuring states of the sensors 12, 14, 16, 18 was carried out. The other components of the transport system 100 of the Figure 2 are among those of the Figure 1 equivalent.

[0038] In the Figure 2 The first subset of sensors 12, 14 is in the second low-frequency measurement state NF, in which acceleration values of the train 10 are recorded and first acceleration data are generated with a recording frequency of less than 50 Hz. Using the acceleration data generated by sensors 12, 14 in the second low-frequency measurement state NF, damage 60' of the rail 20 can be detected. The damage 60' is in the Figure 2 again shown as a break point on the railway sleepers 22 and a rail track 24. The second subset of sensors 16, 18 is located in the Figure 2in the first high-frequency measurement state HF, in which acceleration values of the train 10 are recorded at a recording frequency of 1660 Hz and converted into second acceleration data. By means of the sensors 16, 18 operated in the high-frequency measurement state HF, damage 50' on the rail-bound vehicle 10 can be detected. In the Figure 2 The damage 50' is shown in the form of a vibration of the wagon 10c relative to the railway track 20, indicated by two double arrows. The vibration 50' can be caused, for example, by damage to a wheel and / or a wheel bearing of the wagon 10c.

[0039] How to Figure 1As already explained, the acceleration data recorded by the sensors 12, 14, 16, 18 are transmitted to the communication interface 72 of the control device 70. The acceleration data generated by the sensors 12, 14 operated in the second measurement state are recorded as second measurement data 40' by the control device 70. The acceleration data generated by the sensors 16, 18 operated in the first measurement state are recorded as first measurement data 30' by the control device 70. The control device 70 compares the first measurement data 30' with the first comparison data set 32 to determine the first match according to the procedure described above. The control device 70 compares the second measurement data 40 with the second comparison data set 42 to determine the second match according to the procedure described above.Depending on the first match, the control device 70 detects the damage 50' on the rail-bound vehicle 10 according to the procedure described above. Depending on the second match, the control device 70 detects the damage 60' on the infrastructure element 20 according to the procedure described above.

[0040] In Figure 3 are steps for carrying out the method for detecting damage 50, 50'; 60, 60' on the transport system 100 of the Figures 1 and 2 shown in chronological order. The process begins with step S0. Depending on the type of damage to be detected, the process is divided into two variants. However, as explained above, these can be performed in parallel.

[0041] According to the first variant of the method, in a first measurement data acquisition step Sa1, first measurement data 30, 30' are acquired by at least one sensor 12, 14, 16, 18 operated in a first measurement state NF, HF. In a first match determination step Sb1, a first match of the first measurement data 30, 30' with a first stored comparison data set 32 is determined. Depending on the first match, damage 50, 50' on the rail-bound vehicle 10 is detected in a first damage detection step Sc1.

[0042] According to the second variant of the method, in a second measurement data acquisition step Sa2, second measurement data 40, 40' are acquired by the sensors 12, 14, 16, 18 operated in a second measurement state HF, LF. In a second match determination step Sb2, a second match of the second measurement data 40, 40' with the second stored comparison data set 42 is determined. Depending on the second match, damage 60, 60' on the infrastructure element 20 passable by the rail-bound vehicle 10 is detected in a second damage detection step Sc2.

[0043] In Figure 4 are further on the in Figure 3 The steps applicable to the method shown are shown. These are again divided into two variants, depending on the type of damage to be detected on the transport system 100. The steps Sa1, Sb1 and Sc1, as well as the steps Sa2, Sb2 and Sc2 are Figure 3These steps are referred to in the explanation of the Figure 4 not received again.

[0044] In a first measurement step Sa11 preceding the first measurement data acquisition step Sa1, a first subset of the sensors 12, 14 is operated in a first measurement state HF, LF. Furthermore, if a predetermined condition is present, in a first changeover step Sa111, the measurement state of the first subset of the sensors 12, 14 is changed from the first measurement state HF, LF to a second measurement state LF, HF. Furthermore, in a first class classification step Sb11 following the first match determination step Sb1, the first measurement data 30, 30' are classified into at least two classes depending on the first match. Finally, the first measurement data acquisition step Sa1, the first match determination step Sb1, and the first class classification step Sb11 are repeated several times within a predetermined period of time. The repetition of steps Sa1, Sb1, and Sb11 is indicated by an arrow WH1.In this case, in the first damage detection step Sc1, damage 50, 50' on the rail-bound vehicle 10 is detected if a majority of the first measurement data 30, 30' recorded within the specified period of time were classified into a class that corresponds to damage 50, 50' on the rail-bound vehicle 10 based on the graded damage category.

[0045] In the second variant of the optional procedure implementation according to Figure 4In a second measurement step Sa21 preceding the second measurement data acquisition step Sa2, a second subset of the sensors 16, 18 is operated in a second measurement state NF, HF. Furthermore, if a predetermined condition is present, in a second changeover step Sa211, the measurement state of the second subset of the sensors 16, 18 is changed from the second measurement state NF, HF to a first measurement state HF, NF. Furthermore, in a second class classification step Sb21 optionally following the second match determination step Sb2, the second measurement data 40, 40' are classified into at least two classes depending on the second match. Finally, the second measurement data acquisition step Sa2, the second match determination step Sb2, and the second class classification step Sb21 are repeated several times within a predetermined period of time. The repetition of steps Sa2, Sb2, and Sb21 is indicated by an arrow WH2.In this case, in the second damage detection step Sc2, damage 60, 60' is detected on an infrastructure element 20 passable by the rail-bound vehicle 10 if a majority of the second measurement data 40, 40' recorded within the predetermined period of time have been classified into a class which, based on the graded damage category, corresponds to damage 60, 60' on the rail-bound vehicle 10.

[0046] The proposed method was tested using the Figures 1 to 4explained using the example of a train as a rail-bound vehicle and the example of a rail as an infrastructure element. However, this is by no means to be understood as limiting. The method is equally applicable to the transport system of a cable car, which has a gondola as the rail-bound vehicle and a funicular or a guide rail of the funicular as the infrastructure element. Furthermore, the method is applicable to the transport system of a tram, which has the tram as the rail-bound vehicle and the tram tracks as the infrastructure element. Further embodiments of a transport system with a rail-bound vehicle and an infrastructure element that can be passed by the rail-bound vehicle are also encompassed by the proposed method. Reference symbol

[0047] 10Rail-bound vehicle; Train 10a, 10b, 10cWagons 11Front end section 12, 14, 16, 18Sensors 13Rear end section 20Infrastructure element 22Railway sleeper 24Rail line 26Track bed 30, 30' first measurement data 32first comparison data set 40, 40' second measurement data 42second comparison data set 50, 50' Damage to rail-bound vehicle 60, 60' Damage to infrastructure element 70Control device 72Communication device 100Transport system Sa1First measurement data acquisition step Sa11First measurement step Sa111First change step Sa2Second measurement data acquisition step Sa21Second measurement step Sa211Second change step Sb1First match determination step Sb11First classification step Sb2Second match determination step Sb21Second classification step WH1; WH2Repetition 1; Repetition 2 Sc1First damage detection step Sc2Second damage detection step

Claims

1. Method for detecting damage to a transport system (100), comprising a rail-bound vehicle (10) and an infrastructure element (20) passable by the rail-bound vehicle (10), by means of a plurality of sensors (12, 14, 16, 18) arranged on the rail-bound vehicle (10), the plurality of sensors (12, 14, 16, 18) being operated in a first measuring state and a second measuring state, the method comprising: a first measurement data acquisition step (Sa1) for acquiring first measurement data (30, 30') by way of at least one sensor (12, 14, 16, 18) operated in the first measuring state, the first measurement data acquisition step (Sa1) comprising: specifying a first acquisition frequency of the sensors (12, 14, 16, 18); and acquiring the first measurement data (30, 30') at the specified first acquisition frequency and over a specified first acquisition time; a second measurement data acquisition step (Sa2) for acquiring second measurement data (40, 40') of the sensors (12, 14, 16, 18) operated in the second measuring state, the second measurement data acquisition step (Sa2) comprising: specifying a second acquisition frequency of the sensors (12, 14, 16, 18); and acquiring the second measurement data (40, 40') at the specified second acquisition frequency and over a specified second acquisition time, the first acquisition frequency and the second acquisition frequency differing, and the first acquisition time and the second acquisition time differing; a first match determination step (Sb1) for determining a first match between the first measurement data (30, 30') and a first stored comparison data set (32); a second match determination step (Sb1) for determining a second match between the second measurement data (40, 40') and a second stored comparison data set (42); a first damage detection step (Sc1) for detecting damage (50, 50') to the rail-bound vehicle (10) on the basis of the first match; and a second damage detection step (Sc2) for detecting damage (60, 60') to the infrastructure element (20) passed by the rail-bound vehicle (10) on the basis of the second match.

2. Method according to Claim 1, wherein the sensors (12, 14, 16, 18) mounted on the rail-bound vehicle (10) are configured to use specifiable acquisition frequencies to acoustically measure an acceleration of the rail-bound vehicle (10) relative to the infrastructure element (20) passed by the rail-bound vehicle (10).

3. Method according to Claim 1 or 2, wherein a first subset of the sensors (12, 14) is arranged on a front end section (11) in the direction of travel of the rail-bound vehicle (10) and a second subset of the sensors (16, 18) is arranged on a rear end section (13) in the direction of travel of the rail-bound vehicle (10), the first and second subsets each comprising at least two sensors (12, 14; 16, 18).

4. Method according to Claim 3, comprising: a first measurement step (Sa11) for operating the first subset of the sensors (12, 14) in the first measuring state; and a second measurement step (Sa21) for operating the second subset of the sensors (16, 18) in the second measuring state, the first measurement step (Sa11) and the second measurement step (Sa21) being carried out simultaneously.

5. Method according to Claim 4, comprising: a first change step (Sa111) for changing the measuring state of the first subset of the sensors (12, 14) from the first to the second measuring state if a predetermined condition is satisfied; and a second change step (Sa211) for changing the measuring state of the second subset of the sensors (16, 18) from the second to the first measuring state if the predetermined condition is satisfied.

6. Method according to one of the preceding claims, comprising: a first classification step (Sb11) for putting the first measurement data (30, 30') into at least two classes on the basis of the first match; and a second classification step (Sb21) for putting the second measurement data (40, 40') into at least two classes on the basis of the second match.

7. Method according to Claim 6, wherein the at least two classes are distinguished based on a graded damage category.

8. Method according to Claim 6 or 7, wherein the first classification step (Sb11) comprises: comparing the first measurement data (30, 30') with a subset of the first comparison data set (32); and wherein the second classification step (Sb21) comprises: comparing the second measurement data (40, 40') with a subset of the second comparison data set (42).

9. Method according to Claim 8, wherein the subset of the first or the second comparison data set (32; 42) comprises a plurality of data points, and wherein the comparison is made with the plurality of data points.

10. Method according to one of Claims 6 to 9, wherein the first measurement data acquisition step (Sa1), the first match determination step (Sb1) and the first classification step (Sb11) are repeated multiple times within a specified period and wherein the first damage detection step (Sc1) comprises: detecting damage (50, 50') to the rail-bound vehicle (10) when a plurality of the first measurement data (30, 30') acquired within the specified period have been put into a class that corresponds to damage to the rail-bound vehicle (10) based on the graded damage category.

11. Method according to one of Claims 8 to 10, wherein the second measurement data acquisition step (Sa2), the second match determination step (Sb2) and the second classification step (Sb21) are repeated multiple times within a specified period, wherein the second measurement data (40, 40') comprise a plurality of data points, and wherein the second damage detection step (Sc2) comprises: detecting damage (60, 60') to an infrastructure element (20) passable by the rail-bound vehicle (10) when a predetermined proportion of the data points of the second measurement data (40, 40') acquired within the specified period has been put into a class that corresponds to damage (60, 60') to an infrastructure element (20) passable by the rail-bound vehicle (10) based on the graded damage category.

12. Method according to Claim 11, wherein the predetermined number of the data points of the second measurement data (40, 40') acquired within the specified period is greater than 50%, in particular greater than 90%, of the total data points of the second measurement data (40, 40') acquired within the specified period.

13. Control device (70) comprising a communication interface (72) for receiving measurement data (30, 30'; 40, 40'), the control device (70) being configured to carry out the method according to one of Claims 1 to 12.

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