Method for operating a rail vehicle, monitoring device and computer program product for a rail vehicle

Abstract

The invention relates, inter alia, to a method for operating a rail vehicle. According to the invention, a correlation (K) stored in the vehicle is taken into account in the rail vehicle during operation, said correlation defining a relationship between braking energy, which a wear-prone brake of the rail vehicle absorbs during braking, and the wear of the brake caused thereby.

Description

[0001] 202401242

[0002] 1

[0003] Description

[0004] Procedures and equipment for operating a railway vehicle

[0005] The invention relates to methods and devices for operating railway vehicles.

[0006] The pneumatic braking system of a modern rail vehicle consists of brake pads and brake discs mounted on the axles or wheels. The vehicle is slowed by the controlled regulation of pressure in special actuators (the so-called brake cylinders). In this way, the brake pads exert a controlled pressure on the brake discs via corresponding linkages. Since the brake discs are rigidly connected to the wheels or axles, the pressure exerted on them by the brake pads causes the vehicle's kinetic energy to be dissipated through friction, thus gradually decelerating it. The problem with this braking system lies in the wear of the parts that come into contact during the braking process, namely the brake pads and the brake discs. It is well known from mechanics and tribology that two surfaces sliding against each other experience material abrasion in the contact areas.After numerous braking operations, this leads to obvious wear (thinning or deformation) of the affected components, which then need to be replaced or resurfaced (brake disc resurfacing). To prevent these wear parts from reaching or falling below the minimum thickness limits permitted for railway operation during service, periodic inspections are carried out. These inspections involve visual examination and instrumental measurements of the components directly involved in the braking process. Since these inspections take place at relatively long intervals (usually several tens of thousands of kilometers), maintenance limits are set as a precautionary measure. These limits are generally quite generous to prevent the operating limits from being reached or even exceeded during service.Since the inspection intervals for brake components are specified in the maintenance schedule, the maintenance personnel performing the inspection are also responsible for estimating the remaining service life of the components to be inspected and thus deciding whether or not to replace them preventively (especially the brake pads). Components that are not yet completely worn are often replaced preventively because the remaining service life estimate takes into account the mileage the vehicle must travel before the next inspection. A conservative approach (replacing the pads before reaching the maintenance limits) generally results in good operational reliability, 202401242.

[0007] 2. This is because the operating limits are not reached, but also leads to significant material waste. On the other hand, unforeseen changes (intensification) in operating conditions can accelerate the wear process and reduce thicknesses below minimum limits faster than expected if parts are not replaced with sufficient clearance to fully utilize their remaining potential. This often leads to vehicle damage (e.g., scoring on brake discs) and reduced safety margins regarding the vehicle's braking performance. The fact that the condition of brake system wear parts is only recorded during periodic inspections is therefore a problem for optimal and timely inventory management. Conversely, setting fixed mileage intervals for brake component inspections can lead to material waste or (in the case of less cautious replacement strategies) to greater damage.

[0008] The invention is based on the objective of providing an improved method for operating a rail vehicle.

[0009] This problem is solved according to the invention by taking into account, on the rail vehicle side during operation, a vehicle-side stored correlation which defines a relationship between braking energy absorbed by a wearable brake of the rail vehicle during braking and the resulting wear of the brake.

[0010] A significant advantage of the method according to the invention is that, by taking into account the correlation between braking energy and wear as provided for in the invention, a wear estimate can also be made on the rail vehicle side, and the necessity of brake maintenance can also be estimated on the rail vehicle side depending on the actual operation of the rail vehicle, and appropriate measures can be triggered.

[0011] It is considered advantageous if, on the railway vehicle side, a wear indicator is determined that specifies the wear since a given point in time, taking into account the correlation.

[0012] In a first method variant, considered particularly advantageous, it is provided that a total braking energy is calculated on the rail vehicle side by summing up braking process-specific braking energy values, which describe the braking energy of the individual braking processes carried out since the time of detection, and based on the 202401242

[0013] 3

[0014] The total braking energy and the correlation determine the wear specification on the rail vehicle side.

[0015] In a second method variant, which is considered particularly advantageous, it is provided that for each braking process an individual wear value is determined on the side of the rail vehicle, based on the correlation and an individual braking energy value that describes the braking energy for the individual braking process performed, and the wear value is calculated on the side of the rail vehicle by summing the individual wear values.

[0016] Regardless of the method used to determine the wear specification, it is considered advantageous if, based on an actual condition specification indicating the actual condition of the brake as determined at the time of assessment, and the wear specification, a condition estimate indicating the current condition of the brake is estimated on the railway vehicle side.

[0017] The stored correlation is preferably created on the basis of previous journeys of the rail vehicle and / or other rail vehicles by evaluating measured values ​​that describe braking energy during braking and the observed wear.

[0018] The evaluation of the measured values ​​and the creation of the correlation preferably involves a computer-aided evaluation method of the measured values ​​recorded during previous journeys. Creating the correlation can, for example, include training a neural network that, in its trained state, represents or maps the correlation.

[0019] Correlation can be, for example, a mathematical function or artificial intelligence, such as a trained neural network that generates output values ​​or vectors based on its trained "knowledge" in response to input values ​​or vectors. For instance, in response to an input vector describing braking energy and one or more operating parameters, the artificial intelligence or neural network can output a vector containing wear information, a condition estimate, and / or a forecast. 202401242

[0020] 4

[0021] The correlation is preferably created by including at least one operating parameter describing the operation of the rail vehicle.

[0022] The determination of the wear specification or wear values ​​is preferably carried out by including at least one operating parameter, taking into account the current ferry operation, at least also with regard to this operating parameter.

[0023] It is also advantageous if, on the rail vehicle side, a wear prediction is carried out based on an actual state indication that specifies the actual state of the brake as determined at the time of the assessment, the correlation stored on the vehicle side, and an operating assumption that describes the future journey depending on at least one operating parameter taken into account in the correlation, resulting in a forecast indication.

[0024] The forecast preferably defines a range that the rail vehicle can travel with a functioning brake, and / or a maintenance interval within which brake maintenance should be carried out.

[0025] It is also advantageous if the vehicle checks during the journey whether the operating assumption matches the actual operating situation and, in the event of a deviation between the operating assumption and the actual operating situation, a warning signal is generated and / or the wear forecast is updated taking into account the actual operating situation.

[0026] The forecast information and / or the condition assessment are preferably displayed on a display device on the rail vehicle. Alternatively or additionally, the forecast information and / or the condition assessment can be transmitted to a trackside facility to enable the planning and preparation of maintenance activities.

[0027] The procedure is preferably carried out as a computer-implemented procedure.

[0028] The at least one operating parameter or at least one of the operating parameters is preferably a speed specification that defines the average speed during the journey, or a temperature specification that defines the average temperature during the journey 202401242

[0029] 5 defined, or an operating mode specification that defines an operating mode belonging to a group of predefined operating modes (e.g. freight train, passenger train, local transport, long-distance transport).

[0030] The invention also relates to a monitoring device for a rail vehicle. According to the invention, the monitoring device is designed to carry out a method as described above.

[0031] The monitoring device preferably includes a computing device and a storage device.

[0032] The memory preferably stores a correlation that defines a relationship between the braking energy absorbed by a wear-prone brake of the rail vehicle during braking and the resulting wear of the brake.

[0033] The memory preferably also contains a computer program product with program instructions which, when executed by the computing device, cause it to execute the procedure as described above, taking into account the stored correlation.

[0034] The invention also relates to a computer program product for a rail vehicle. According to the invention, the computer program product comprises program instructions which, when executed by a computing device, cause the device to execute a method as described above.

[0035] The invention is explained in more detail below using exemplary embodiments, including the following:

[0036] Figure 1 shows an embodiment of a monitoring device according to the invention, in which braking energy values ​​specific to each braking process are summed up.

[0037] Figure 2, as a second embodiment, shows the embodiment according to Figure 1 in computer-implemented form, 202401242

[0038] 6

[0039] Figure 3, as a third embodiment, shows a configuration of the monitoring device according to the invention, in which wear values ​​specific to each braking process are summed up.

[0040] Figure 4, as the fourth embodiment, shows the embodiment according to Figure 3 in computer-implemented form, and

[0041] Figure 5 shows, as a fifth embodiment, a variant of a monitoring device without its own correlation generation device.

[0042] For the sake of clarity, the same reference symbols are always used in the figures for identical or comparable components.

[0043] Figure 1 shows a first embodiment of a monitoring device 10 according to the invention for a rail vehicle not shown in further detail, with which methods according to the invention can be carried out.

[0044] The monitoring device 10 according to Figure 1 comprises a

[0045] Brake energy determination device 11, an adding device 12 downstream of the brake energy determination device 11, a wear determination device 13 downstream of the adding device 12 and a condition estimation device 14 downstream of the wear determination device 13.

[0046] The wear detection device 13 uses a correlation K stored in the monitoring device 10 according to Figure 1, which is also used by a wear prediction device 15 of the monitoring device 10.

[0047] The correlation K describes a relationship between braking energy absorbed by a wear-prone brake of the rail vehicle during braking and the resulting wear of the brake.

[0048] The correlation K can, for example, be a mathematical function or artificial intelligence, such as a trained neural network that generates output values ​​or vectors based on trained "knowledge" in response to input values ​​or vectors. For example, the artificial intelligence or neural network could, in response to braking energy and one or more 202401242

[0049] 7

[0050] The operating parameters are described by an input vector that outputs a vector containing a wear value VA, a condition estimate ZSA, or a forecast PA.

[0051] In the embodiment shown in Figure 1, the correlation K is determined or generated by a correlation generation device 20, which can also be part of the monitoring device 10. Alternatively, the correlation K can be generated by an external device and stored in a memory of the monitoring device 10 for further use.

[0052] The arrangement shown in Figure 1 can be operated as follows, for example:

[0053] The braking energy determination device 11 determines, based on input measurement values ​​M, a braking energy value Wi specific to each braking operation of a brake (not shown) of the rail vehicle. This value describes the braking energy of the brake for the i-th braking operation performed. The measurement values ​​M used to calculate the braking energy values ​​Wi can, for example, describe the brake pressure, the speed profile during braking of the rail vehicle, the mass of the braked rail vehicle, and / or a braking stage.

[0054] The adding device 12, which is subordinate to the brake energy determination device 11, adds up the brake process-specific brake energy values ​​Wi, which have been entered since a predetermined determination time tO, to form a total energy value Wg and forwards this total energy value Wg to the wear determination device 13, which is subordinate to it.

[0055] The wear determination device 13 determines a wear value VA, which indicates the wear of the brake since the time of determination tO, taking into account the correlation K and the total energy value Wg transmitted by the adding device 12.

[0056] The correlation generation device 20 determines the correlation K between braking energy and brake wear, for example, based on a large number of measured values ​​M that quantitatively describe braking energies during braking operations and the brake wear observed during these braking operations. The correlation K is preferably designed, or in the case of artificial intelligence, preferably trained, such that it 202401242

[0057] 8. In addition, one or more operating parameters BP are taken into account during the operation of the rail vehicle.

[0058] A suitable operating parameter BP is, for example, a speed value that defines the average speed during the journey of the rail vehicle. By including such an operating parameter, the correlation K can, for instance, take into account that a higher average speed is to be expected to result in greater brake wear than a comparatively low average speed. The influence of average speed on wear can be determined quantitatively using static evaluation methods or, within the framework of training artificial intelligence, using appropriate training data that, in addition to measured values ​​M of braking energies during braking processes and the observed brake wear, considers the average speed as a training parameter.

[0059] Another suitable operating parameter BP is, for example, a temperature value that defines the average ambient temperature during the train's operation. By including such an operating parameter, the correlation K can, for instance, take into account that higher brake wear is to be expected at a generally high ambient temperature than at a comparatively low ambient temperature. The influence of the ambient temperature on wear can be determined quantitatively using static evaluation methods or, within the framework of artificial intelligence training, using appropriate training data that, in addition to measured values ​​M of braking energies during braking processes and the observed brake wear, considers the ambient temperature as a training parameter T.

[0060] Another suitable operating parameter BP is, for example, an operating mode specification that defines an operating mode belonging to a group of predefined operating modes (e.g., freight train, passenger train, local transport, long-distance transport). By including such an operating parameter, the correlation K can, for example, take into account that very heavy rail vehicles, such as freight trains, are expected to experience higher brake wear than comparatively light rail vehicles. The influence of the operating mode specification on wear can be determined quantitatively using static evaluation methods or, within the framework of artificial intelligence training, using appropriate training data that includes the operating mode specification as a training parameter, in addition to measured values ​​M for braking energies during braking processes and the observed brake wear. 202401242

[0061] If the correlation K takes into account one or more operating parameters BP, in particular one or more of the operating parameters mentioned above, the wear determination device 13 preferably also takes these into account when determining the wear specification VA by considering the current parameter values ​​of the rail vehicle for these operating parameters BP.

[0062] The wear information VA is sent to the condition estimation device 14, which, based on an actual condition information IZA, which indicates an actual state of the brake as determined at the time of assessment tO, and the wear information VA, estimates a condition assessment ZSA indicating the current state of the brake at time t, for example by adding the wear information VA to the actual condition information IZA, if the latter is also a wear information.

[0063] For the purpose of wear prediction, the aforementioned wear prediction device 15 is used, which also utilizes the correlation K. Depending on the actual state information IZA, which indicates the actual state of the brake as determined at the specified time tO, the vehicle-stored correlation K, and an operating assumption A1, which describes the future journey depending on at least one operating parameter BP considered in the correlation K, the wear prediction device 15 performs a wear prediction, generating a prediction PA.

[0064] Operating assumption A1 can, for example, describe that the rail vehicle is a freight train traveling at high speed at an average temperature of 10 degrees Celsius. Furthermore, the operating assumption can include route information that describes, for example, the braking processes expected for the rail vehicle, such as a specification that quantifies the expected braking energy per kilometer traveled.

[0065] Using the information contained in the operating assumption A1, the wear prediction device 15 can, for example, generate a prediction PA that defines a range the rail vehicle can travel with a functioning brake and / or a maintenance interval within which brake maintenance should be carried out. 202401242

[0066] 10

[0067] During operation, the wear prediction device 15 checks whether the operating assumption A1 corresponds to the actual operating situation. If the operating assumption A1 deviates from an operating situation specification A2 describing the actual operating situation beyond a predefined limit, it generates a warning signal WS and / or updates the wear prediction taking the actual operating situation into account.

[0068] The condition estimate ZSA generated by the condition estimation device 14 and the forecast PA generated by the wear prediction device 15 can, for example, be forwarded to a control unit 30 of the rail vehicle, which can display the forecast PA and the condition estimate ZSA, for example, on a display device in the rail vehicle or forward them to a trackside control center or the like.

[0069] Figure 2 shows a second embodiment of a monitoring device 10 according to the invention, with which methods according to the invention can be carried out as computer-implemented methods. The monitoring device 10 is integrated into a computer system 100, which can, for example, be a component of a vehicle control unit of the rail vehicle. The computer system 100 comprises a computing unit 110 and a memory 120 in which a computer program product CPP containing software modules is stored, which, when executed by the computing unit 110, can carry out a method according to the invention.

[0070] The computer program product CPP comprises a brake energy determination module SW11, which, when implemented by the computing device 110, forms the brake energy determination device 11 according to Figure 1, an adding module SW12, which, when implemented by the computing device 110, forms the adding device 12 according to Figure 1, a wear determination module SW13, which, when implemented by the computing device 110, forms the wear determination device 13 according to Figure 1, a condition estimation module SW14, which, when implemented by the computing device 110, forms the condition estimation device 14 according to Figure 1, and a wear prediction module SW15, which, when implemented by the computing device 110, forms the wear prediction device 15 according to Figure 1.

[0071] In addition, the correlation K and, for example, a vehicle control module SW30 are stored in memory 120 of the computer 100. When executed by the computer 110, this module forms the control unit 30 according to Figure 1. 202401242

[0072] 11

[0073] Regarding the operation of the computer system 100 and the functionality of the computer program product CPP, the above statements apply accordingly in connection with Figure 1.

[0074] The computer program product CPP can also include a correlation creation module SW20 for determining the correlation K, which, when executed by the computing device 110, forms the correlation creation device 20 according to Figure 1, for example by training a neural network on the basis of training data which, in addition to measured values ​​M for braking energies during braking processes and the observed wear of the brake, take into account further operating parameters BP (such as average speed, average temperature or operating mode specification) as training parameters.

[0075] Figure 3 shows a third embodiment of a monitoring device 10 according to the invention, with which methods according to the invention can be carried out as computer-implemented methods. In this embodiment, the adding device 12 is not connected between the brake energy determination device 11 and the wear determination device 13, but between the wear determination device 13 and the condition estimation device 14.

[0076] The wear detection device 13 calculates a brake-process-specific wear value Vi for each brake-process-specific brake energy value Wi and transmits this to the downstream adding device 12. The wear detection device 13 uses the correlation K and the applicable operating parameters BP for this purpose, as explained above.

[0077] The adding device 12 adds all brake-process-specific wear values ​​Vi calculated since the predetermined measurement time tO to form the wear value VA, which indicates the brake wear since the measurement time tO. The wear value VA is then passed to the condition estimation device 14, which performs the estimation of the condition value ZSA.

[0078] Furthermore, the above statements apply accordingly in connection with Figures 1 and 2. For example, the monitoring device 10 according to Figure 3 can be formed by the software modules SW11 to SW15 shown in connection with Figure 2, as shown in Figure 4. 202401242

[0079] 12

[0080] Figure 5 shows a fifth embodiment of a monitoring device 10 according to the invention, with which methods according to the invention can be carried out. In this embodiment, the correlation generation device 20 is not a component of the computer program product CPP; the correlation K is generated by another software module stored in the memory 120 or by an external device not belonging to the computer system 100.

[0081] Finally, it should be mentioned that the features of all the embodiments described above can be combined with each other in any way to form further embodiments of the invention.

[0082] Furthermore, all features of dependent claims can each be combined with each of the subordinate claims, either individually or in any combination with one or more other dependent claims, to obtain further embodiments.

[0083] Regardless of the grammatical gender of a particular term, persons with male, female or other gender identities are included.

[0084] 202401242

[0085] Reference symbol list

[0086] 10 Monitoring device

[0087] 11 Brake energy measurement device

[0088] 12 Adding device

[0089] 13 Wear detection device

[0090] 14 Condition assessment device

[0091] 15 Wear prediction device

[0092] 20 Correlation creation device

[0093] 30 Control unit

[0094] 100 computer systems

[0095] 110 computer equipment

[0096] 120 storage

[0097] A1 Operating Reception

[0098] A2 Operating situation description

[0099] BP operating parameters

[0100] CPP Computer Program Product

[0101] IZA Current Status Report

[0102] K correlation

[0103] M measured value

[0104] PA forecast

[0105] SW1 1 braking energy determination module

[0106] SW12 Adder Module

[0107] SW13 Wear Detection Module

[0108] SW14 Condition Estimation Module

[0109] SW15 Wear Prediction Module

[0110] SW20 Correlation Creation Module

[0111] SW30 Vehicle control module t Time tO Determination time

[0112] VA wear indicator

[0113] Vi brake-process-specific wear value

[0114] Regarding the total energy rating

[0115] Wi braking process-specific braking energy value

[0116] WS warning signal

[0117] ZSA condition assessment

Claims

202401242 14 Patent claims 1. Method for operating a rail vehicle, characterized in that, during operation, a vehicle-side stored correlation (K), which defines a relationship between braking energy absorbed by a wear-prone brake of the rail vehicle during braking and the resulting wear of the brake, is taken into account.

2. Method according to claim 1, characterized in that, on the rail vehicle side, a wear indication (VA) specifying the wear since a predetermined determination time (tO) is determined taking into account the correlation (K).

3. Method according to claim 2, characterized in that - on the rail vehicle side, a total braking energy (Wg) is calculated by summing up braking process-specific braking energy values ​​(Wi), which describe the braking energy of the individual braking processes carried out since the time of measurement, and - the wear rating (VA) is calculated on the side of the rail vehicle based on the total braking energy and the correlation (K).

4. Method according to claim 2, characterized in that - for each braking operation on the rail vehicle side, a braking operation-specific wear value (Vi) is determined, based on the correlation (K) and a braking operation-specific braking energy value (Wi), which describes the braking energy for the respective individual braking operation performed, and - the wear specification (VA) is calculated by summing the brake-process-specific wear values ​​(Vi) on the rail vehicle side.

5. Method according to one of the preceding claims 2 to 4, characterized in that, based on an actual state specification (IZA), which indicates the actual state of the brake as determined at the time of determination (tO), and the wear specification (VA), a 202401242 15. The condition estimate (ZSA) indicating the current state of the brake is estimated on the rail vehicle side.

6. Method according to one of the preceding claims, characterized in that the stored correlation (K) has been created on the basis of previous journeys of the rail vehicle and / or other rail vehicles by evaluating measured values ​​that describe the braking energy during braking and the observed wear.

7. Method according to one of the preceding claims, characterized in that - the correlation (K) was created taking into account at least one operating parameter (BP) describing the operation of the rail vehicle and - the formation of the wear specification (VA) or the wear values ​​(Vi) is carried out taking into account at least one operating parameter (BP), by considering the current ferry operation at least also with regard to this operating parameter (BP).

8. Method according to one of the preceding claims, characterized in that, on the rail vehicle side, a wear prediction is carried out based on an actual state specification (IZA) which indicates the actual state of the brake as determined at a time of determination (tO), the correlation (K) stored on the vehicle side and an operating assumption which describes the future journey depending on at least one operating parameter (BP) taken into account in the correlation (K), forming a prediction specification (PA).

9. Method according to claim 8, characterized in that the forecast specification (PA) defines a range that the rail vehicle can travel with a functioning brake, and / or a maintenance interval within which maintenance of the brake is to be carried out.

10. Method according to any one of the preceding claims 8 to 9, characterized in that 202401242 16. During the further journey, the vehicle checks whether the operating assumption corresponds to the actual operating situation, and in the event of a deviation between the operating assumption and the actual operating situation, a warning signal (WS) is generated and / or the wear forecast is updated taking into account the actual operating situation.

11. Method according to one of the preceding claims, characterized in that the method is a computer-implemented method.

12. Method according to one of the preceding claims, characterized in that the at least one operating parameter (BP) or at least one of the operating parameters (BP) - a speed rating that defines the average speed during the journey, or - a temperature reading that defines the average temperature during the journey, or - an operating mode specification is one that defines an operating mode belonging to a group of predefined operating modes.

13. Monitoring device (10) for a rail vehicle, characterized in that the monitoring device (10) is designed to perform a method according to one of the preceding claims.

14. Monitoring device (10) according to claim 13, characterized in that - the monitoring device (10) comprises a computing device (110) and a storage device (120), - wherein a correlation (K) is stored in the memory, defining a relationship between braking energy (Wi, Wg) absorbed by a wear-prone brake of the rail vehicle during braking and the resulting wear (V) of the brake, and - wherein the memory (120) also contains a computer program product (CPP) with program instructions which, when executed by the computing device (110), cause it to execute the method according to one of the preceding claims, taking into account the stored correlation (K). 202401242 17 15. Computer program product (CPP) for a rail vehicle, characterized in that the computer program product (CPP) comprises program instructions which, when executed by a computing device (110), cause it to execute the method according to one of the preceding claims.

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