Forecasting wear characteristics of rail vehicles

Abstract

A method for determining time-dependent wear characteristics (VV) of a technical component of a rail vehicle is described. The method involves determining and storing a digital twin (ZW) of the technical component with a current wear status (VS) and a distance-dependent wear profile (V) of the technical component. Furthermore, the future time-dependent wear characteristics (VV) of the technical component of the rail vehicle are estimated according to a future timetable (FP) of the rail vehicle and on the basis of the digital twin (ZW). A method for scheduling rail vehicles is also described. Additionally, an estimating device (50) is described. Moreover, a planning device (60) is described.

Description

[0001] Description

[0002] Predicting the wear behavior of rail vehicles

[0003] The invention relates to a method for determining a time-dependent wear behavior of a technical component of a rail vehicle. Furthermore, the invention relates to a method for planning the deployment of rail vehicles. Furthermore, the invention relates to an estimation device. Furthermore, the invention relates to a planning device.

[0004] The wear and tear of functional units in rail vehicles must be regularly monitored by maintenance personnel in order to avoid breakdowns and accidents due to technical failure of the functional unit. Regular visual inspection of wear-prone technical components is time-consuming and also costs valuable operating time. Automated prediction of material wear in the railway environment, on the other hand, would enable improved and plannable maintenance intervals and therefore cost savings in the optimal operation of rail vehicles, particularly locomotives. Savings potential arises particularly in components subject to high levels of wear, such as the contact strips on pantographs or wheels. Individual wear patterns are usually determined by local conditions. Such local conditions include, for example, the type and level of the mains voltage used, the degree of wear on the overhead line, etc.

[0005] Traditionally, maintenance or replacement of components is carried out after a defined number of kilometers traveled or after a measurement of the wear strip thickness in the depot. However, the former approach does not take into account the individual wear behavior of technical components, so that material resources cannot be used optimally. While the latter approach results in high maintenance costs for checking the wear status of the respective technical component, so that in this case the personnel costs are comparatively high. The task therefore exists to enable a more effective prediction of the future wear of a rail vehicle and more effective maintenance of a rail vehicle.

[0006] This object is achieved by a method for determining a time-dependent wear behavior of a technical component of a rail vehicle according to patent claim 1, a method for planning the deployment of rail vehicles according to patent claim 8, an estimation device according to patent claim 12 and a planning device according to patent claim 13.

[0007] In the method according to the invention for determining a time-dependent wear behavior of a technical component of a rail vehicle, a digital twin of the technical component, which includes a current wear status and a route-dependent wear profile of the technical component of the rail vehicle, is determined and stored. A technical component is to be understood as a technical functional unit of the rail vehicle used in ferry operation of a rail vehicle, which is subject to use-dependent, in particular route-dependent, wear. Colloquially, such a technical component is also referred to as a wearing part. If the technical component reaches a maximum tolerable level of wear, it must either be replaced or overhauled or repaired as part of a maintenance measure.A digital twin is a digital representation of a technical component that contains information about the physical or technical condition of the technical component. In this specific case, the digital twin represents the current wear status and location-dependent wear behavior of a technical component of a rail vehicle.

[0008] The current wear status indicates the most recently measured value of wear on the technical component. A wear profile is understood to be a location-dependent measure of wear on the technical component. The location dependency is preferably implemented by dividing the route network into route sections and determining the extent of wear on the technical component assigned to each route section. Since the wear on the technical component is generally very low during a single journey over a route section, the cumulative wear of a rail vehicle is measured after a large number of journeys on the route section and, on the basis of the cumulative wear, the average wear on the rail vehicle for a single journey over the route section is calculated.This average wear of a technical component during a journey over a specific section of track ultimately yields the wear profile of the rail vehicle, also known as the track-specific wear profile component, for this specific section of track. A track section is formed by a rail line between two stations. Preferably, the track section is formed by a rail line between two adjacent stations.

[0009] In this way, a location-dependent wear profile can be determined for different technical components of an individual rail vehicle. The wear profile can also be generalized and parameterized. This means that the wear profile is preferably determined on the basis of wear values ​​from observations of a large number of rail vehicles and scenarios with different parameters or parameter values. As explained in more detail later, the wear of a pantograph or of wheels on a rail vehicle can depend on weather conditions and the weight of the rail vehicle. Instead of an average value for wear, a wear profile for each section of track can also be assigned a function which depends on different variables and / or parameters that influence the wear of a technical component.

[0010] Furthermore, the future time-dependent wear behavior of the rail vehicle is estimated based on the future timetable of the rail vehicle and the digital twin. As already mentioned, the digital twin includes both the current wear status of the technical components of the rail vehicle and a route-dependent wear profile, on the basis of which the future time-dependent wear behavior of the rail vehicle is estimated.

[0011] The timetable can be used to determine the future route and track usage of a rail vehicle. Based on the route, the track sections assigned to the route can be determined. Based on the wear profile values ​​or wear profile functions assigned to the individual track sections and known and predicted values ​​for variables and parameters, predicted wear values ​​for an individual rail vehicle can be calculated for individual track sections. The predicted total wear, assuming a timetable is met within a predetermined time interval, is then the sum of the predicted wear values ​​for each track section.

[0012] Advantageously, a maintenance interval or the next maintenance date can be determined based on the predicted wear of a technical component of a rail vehicle. Conventionally fixed, flat-rate maintenance intervals can thus be individually adjusted using the method according to the invention, so that the maintenance effort can be reduced to the necessary minimum. The maintenance of an individual rail vehicle can therefore be planned based on the timetable. Rail vehicles can also be used more optimally for specific driving missions, taking into account their current wear status and their respective predicted next maintenance date.

[0013] On the basis of the above considerations and the advantageous effects described, a method for planning the deployment of rail vehicles can also be specified. In the method according to the invention for planning the deployment of rail vehicles, a future wear behavior of a plurality of available rail vehicles for different deployment scenarios of the rail vehicles is determined in order to fulfill a predetermined timetable using the method according to the invention for determining a time-dependent wear behavior of a technical component of a rail vehicle. Furthermore, a deployment scenario is selected depending on a secondary condition related to the future wear behavior or the predicted wear and the necessary maintenance of the rail vehicles. The secondary condition preferably comprises one or more wear- and maintenance-related criteria to be fulfilled.An operational scenario is understood to be a plan according to which certain vehicles are deployed on specific routes according to a timetable in order to fulfill the aforementioned secondary condition. For example, such a criterion can require minimal maintenance effort within a predetermined time interval. The use of rail vehicles can also be adapted to maintenance capacities. For example, the wear and tear of the rail vehicles is controlled in such a way that, with a minimum number of rail vehicles to be kept on hand and with existing or specified maintenance capacities, there is always sufficient maintenance capacity available for the rail vehicles, thus avoiding timetable disruptions due to maintenance backlogs and the like.

[0014] The estimation device according to the invention has a database for determining and storing a digital twin of a technical component of a rail vehicle with a current wear status and a route-dependent wear profile of the technical component of the rail vehicle. Furthermore, the estimation device according to the invention comprises an estimation unit for estimating the future time-dependent wear behavior of the technical component of the rail vehicle as a function of a future timetable of the rail vehicle and on the basis of the digital twin. As already mentioned, the digital twin comprises information about a current wear status of the technical component of the rail vehicle and information about a route-dependent wear profile of the technical component of the rail vehicle.The estimation device according to the invention shares the advantages of the method according to the invention for determining a time-dependent wear behavior of a technical component of a rail vehicle.

[0015] The planning device according to the invention comprises an estimation device according to the invention for estimating wear and tear on a plurality of available rail vehicles for different deployment scenarios of the rail vehicles to fulfill a predetermined timetable, and a selection unit for selecting a deployment scenario depending on a secondary condition related to the wear and tear and the necessary maintenance of the rail vehicles. The planning device according to the invention shares the advantages of the method according to the invention for deployment planning of rail vehicles.

[0016] A large part of the aforementioned components of the estimation device and planning device according to the invention can be implemented wholly or partially in the form of software modules in a processor of a corresponding computer system, e.g., by a computer in a central office of a transport company or in a railway depot. A largely software-based implementation has the advantage that even previously used computer systems can be easily upgraded by a software update to operate in the manner according to the invention. In this respect, the object is also achieved by a corresponding computer program product with a computer program that can be loaded directly into a computer system, with program sections for implementing the steps of the method according to the invention for determining a time-dependent wear behavior of a technical component of a rail vehicle and the method according to the invention for the deployment planning of rail vehicles.at least the steps that can be carried out by a computer, in particular the step of determining and storing a digital twin, the step of estimating the future time-dependent wear behavior of the rail vehicle, the step of estimating wear of a plurality of available rail vehicles, and the step of selecting an application scenario depending on a secondary condition related to the wear and the necessary maintenance of the rail vehicles, when the program is executed in the computer system. Such a computer program product may, in addition to the computer program, optionally include additional components, such as documentation, and / or additional components, including hardware components, such as hardware keys (dongles, etc.) for using the software.

[0017] A computer-readable medium, e.g., a memory stick, a hard disk, or another portable or permanently installed data storage device, on which the program sections of the computer program that can be read and executed by a computer system are stored, can be used for transport to the computer system or control unit and / or for storage on or in the computer system or control unit. For this purpose, the computer system can, for example, have one or more cooperating microprocessors or the like.

[0018] The dependent claims and the following description each contain particularly advantageous embodiments and developments of the invention. In particular, the claims of one claim category can also be developed analogously to the dependent claims of another claim category and their description parts. Furthermore, within the scope of the invention, the various features of different embodiments and claims can also be combined to form new embodiments.

[0019] In the method according to the invention for determining a time-dependent wear behavior of a technical component of a rail vehicle, the technical component preferably comprises a technical component for energy transmission. The energy transmission preferably comprises a transmission of electrical energy and / or mechanical energy. The transmission of energy gives rise to a multitude of dissipation phenomena, whereby the component or components for energy transmission are worn out. The technical component also preferably comprises a component which moves relative to a contact element, which is preferably at rest. The energy transmission can comprise both the energy supply of the rail vehicle and also part of the traction process. The relative movement gives rise to friction effects which lead to wear of the technical component.Most preferably, the contact element is part of the stationary railway infrastructure, with the technical component moving relative to the contact element. Since the wear of a technical component in direct contact with the infrastructure is generally route-dependent, it is particularly effective to estimate the future time-dependent wear behavior of a technical component of a rail vehicle based on a route-dependent wear profile, which is part of a digital twin of the technical component of the rail vehicle, and a future timetable of the rail vehicle.

[0020] In addition to energy transmission at an interface between the rail vehicle and the infrastructure, such as an overhead line or the tracks used, the technical component can also be part of a technical device installed inside the rail vehicle for electrical or mechanical energy transmission or energy conversion. Even with an internal technical component of a rail vehicle, varying stress on the technical component depending on the route can have an individual influence on the extent of its wear.

[0021] Particularly preferably, the technical component comprises one of the following component types:

[0022] - a sanding strip,

[0023] - a wheel profile .

[0024] The current wear status for the grinding bar preferably includes information regarding the current wear condition or wear status of the grinding bar. This information preferably includes the current grinding bar thickness.

[0025] The current wear status of a wheel profile preferably includes information regarding the current wear condition or wear status of the wheel profile. This information preferably includes quantitative information regarding a deviation of the current wheel profile from a wheel profile of an unused wheel of a rail vehicle of the same type. Advantageously, the maintenance of these particularly wear-prone technical components can be optimized, and the use of rail vehicles can be coordinated and optimized based on the current and predicted wear of the individual rail vehicles.

[0026] As already mentioned, the estimation of the future wear-dependent wear behavior of the technical components of the rail vehicle is based on the current wear status and the route-dependent wear profile of the rail vehicle, which are each part of the stored digital twin. Advantageously, no additional investigation is required for the wear forecast; it is sufficient to query the current status data of the digital twin.

[0027] The wear profile preferably comprises a digital rail network map to which location-dependent attributes which influence the wear profile are assigned. These location-dependent attributes enable the creation of a “wear map” which permits a forecast of the wear of a technical component depending on the selected route of the rail vehicle. Location-dependent attributes can include, for example, the type of traction power network used, the degree of wear of the overhead line and a location-dependent incline or decline. A digital rail network map makes it possible to assign attributes which influence the wear profile to points or sections or sections of track. Digital maps can be based on map material, for example detailed map material available on the Internet such as “Openrail” or “OpenStreetMap”, which depicts routes as graphs.

[0028] The digital rail network map preferably depicts the routes as graphs with edges connecting neighboring stations, preferably railway stations. Wear profile components specific to specific sections are preferably assigned to the edges of the graph. For a given timetable, total wear can advantageously be calculated by summing the values ​​of wear profile components specific to specific sections of different sections.

[0029] Alternatively, wear patterns can also be stored in nodes of a graph if a technical component is not subject to continuous wear, but to discrete, "intermittent" wear. Such instantaneous wear is preferably caused by a temporary disruption in the infrastructure that results in intermittent wear. Such an effect can occur, for example, in the case of a defective / worn rail joint or when sleepers sink. A worn overhead line can also produce such an effect. Pressure differences generated at tunnel entrances and exits, which generate unwanted lateral forces and thus create their own wear patterns, can also cause instantaneous wear.

[0030] The section-related wear profile component preferably comprises at least one of the following data types:

[0031] - statistics on the wear and tear of the technical components,

[0032] - weather data,

[0033] - Vehicle data .

[0034] Using statistics regarding the wear and tear of the technical component occurring in each section of the route, an expected value can be calculated for the wear and tear of the technical component that occurs when a predetermined timetable is fulfilled.

[0035] Weather data provides information about current and expected weather conditions when fulfilling a timetable. These weather conditions influence the wear and tear of a rail vehicle's technical components.

[0036] Vehicle data or vehicle-specific data also influence the expected wear of a technical component of a rail vehicle. For example, the wear of a contact strip and a wheel of a rail vehicle depends on the weight of the rail vehicle. Typical vehicle-specific properties include the type of pantograph used by a rail vehicle.

[0037] The vehicle data preferably comprises at least one of the following data types: - vehicle characteristics,

[0038] - Mission data,

[0039] - Maintenance data .

[0040] The vehicle characteristics include individual properties of a rail vehicle, for example a vehicle version or a vehicle type.

[0041] Mission data refers to all data influencing the wear and tear of technical components related to the individual ferry operations of the individual rail vehicles. Mission data includes, in particular, the routes traveled and their length and condition.

[0042] The maintenance data includes the current wear condition, preferably the wear strip thickness or wheel rim thickness and, if applicable, also performed or planned maintenance activities.

[0043] The type of route traveled influences the wear behavior of various technical components of a rail vehicle. For example, wheels or contact strips wear more rapidly on routes with steep gradients than on level routes. Wear on contact strips and wheels is also significantly increased at higher speeds. Wheel wear also depends on the geometry of the wheels and the track traveled on.

[0044] In addition to the direct storage of statistical data, functional relationships for calculating wear, either globally or for individual track sections, can also be stored in the digital twin. For example, as already briefly mentioned, wear on a technical component can be represented as a linear or non-linear function of a number of variables and parameters. Such parameters preferably include the length of a track section, the average speed of a rail vehicle on the track, and, specifically for a contact strip, the current density through the contact strip.

[0045] Alternatively or additionally, instead of a rigid model for the wear of a technical component depending on the variables and parameters influencing wear, a model based on artificial intelligence can be determined and saved. Typical methods for generating such a model include machine learning. Such a model is preferably trained individually for a plurality of route sections, particularly preferably for each route section of a route network or each edge of a graph assigned to the route network. So-called labeled training data is used for this purpose, which has both known values ​​of variables and parameters as input data and wear values ​​as labels that can be compared with the result data generated during training. Advantageously, such a model can even be retrained or re-trained while a rail vehicle is traveling.updated if parameter values ​​and values ​​for variables and wear values ​​of the technical components are measured during or after this, so that the model is more accurate and up-to-date. It should be noted that wear on a technical component, for example a contact strip or a wheel tire, is generally smaller than the measurement tolerance of the measuring instruments used to measure wear. Therefore, wear can only be measured with the required accuracy after a number of journeys over a section of track. A model based on artificial intelligence has the advantage of improved flexibility and scalability, as the model is automatically adapted to changing conditions and, in contrast to determining expected wear based on statistics, less data needs to be kept.Such a model preferably includes a regressor. A regressor allows a functional relationship to be determined based on statistical data.

[0046] In the method for operational planning according to the invention, the constraint preferably includes the maximum maintenance capacity for the rail vehicles. Taking the maintenance capacity into account can advantageously maintain scheduled ferry operations.

[0047] Preferably, the constraint includes minimal maintenance effort for the rail vehicles. This advantageously allows for savings in resources for maintaining the rail vehicles.

[0048] The constraint also preferably includes fulfilling the timetable without maintenance. This option is intended to avoid having to temporarily take vehicles out of service for maintenance. This approach can be useful when very few rail vehicles are available and maintenance-related downtimes are to be avoided.

[0049] The constraint may also include fulfilling the timetable with as few rail vehicles as possible. Simultaneous maintenance operations on different vehicles should be kept to a minimum in order to keep as many vehicles as possible in service simultaneously.

[0050] To create the digital twin, the extent of wear on the technical component is preferably determined automatically. Advantageously, the creation of a wear twin can be done without personnel expenditure.

[0051] The invention is explained in more detail below with reference to the accompanying figures using exemplary embodiments. They show: FIG. 1 shows a graph of a train connection illustrating the wear behavior of a contact strip of a rail vehicle.

[0052] FIG 2 is a diagram illustrating the dependence of wear behavior on a plurality of parameters,

[0053] FIG 3 a flow chart illustrating an estimation of wear behavior based on an AI-based model,

[0054] FIG 4 is a flowchart illustrating a planning method according to an embodiment of the invention,

[0055] FIG 5 is a schematic representation of an estimation device according to an embodiment of the invention,

[0056] FIG 6 is a schematic representation of a planning device according to an embodiment of the invention.

[0057] FIG 1 shows a graph 1 of a section of a digital network map with a train connection. The graph 1 illustrates the wear behavior of a contact strip of a rail vehicle. The train connection runs between Berlin (abbreviated as B) via Leipzig (abbreviated as L), Nuremberg (abbreviated as N) to Munich (abbreviated as M). At the edges of the graph, wear information V is shown as route-related wear profile components VK. BL , V LN , V NM, for sections between the individual cities for a contact strip of a rail vehicle is shown or arranged. Of course, such wear information can also include information on the wear of a plurality of contact strips on different rail vehicles. The wear information can also include statistics on the wear of a contact strip for a plurality of rail vehicles. Furthermore, the wear profile can show a dependency of the wear on current weather data or characteristic vehicle data. Therefore, on the basis of the statistical data stored for each edge of the graph and, if necessary, functional relationships, with knowledge of the vehicle type and the forecast weather, a statistically expected wear or a statistically expected wear profile of a specific rail vehicle that is to travel a predetermined route can be calculated.If the current contact strip thickness of a contact strip of a specific rail vehicle is known, the aforementioned data can be used to estimate when the rail vehicle needs maintenance according to a predetermined timetable. The data collection represented by the graph 1 shown in FIG. 1 is also referred to as a digital twin (ZW) or wear twin.

[0058] In FIG 2, a diagram 200 is shown which is a model representation or a non-linear function of a wear A (in micrometers pm per kilometer km) as a function of variables and parameters, such as the speed v (in kilometers km per hour h) and the electrical current density J (in amperes A per square centimeters cm 2) by the contact strip. This variant allows a prediction of wear during a train journey on a predetermined track section to be determined without having to directly access a database with extensive statistics.

[0059] FIG 3 shows a flow chart 300 which illustrates an estimation of a wear behavior of a pantograph based on an AI-based model.

[0060] In step 3.1, data regarding the wear profile A(P) of a pantograph type is collected for each section or each edge of a graph representing a route map. A specific pantograph type is defined for each edge, depending on the power system used on the associated route. If, for example, a pantograph of a first type has to be used on a route section, it is not sensible to also display values ​​for a second type of pantograph that is technically different from the first. Likewise, the type of traction current is defined for each route section, i.e. whether, for example, DC (direct current) or AC (alternating current) is used and the electrical voltage used by the traction current network in this section, e.g. 1.5 kV, 3 kV, 16.7 kV or 25 kV.However, for a longer planned route, different pantograph types and different types of traction current can be used by the same rail vehicle. Other parameters P that influence pantograph wear include the speed of the rail vehicle, weather data, and train weight. The train weight influences the electrical current density J of the current flowing through the pantograph. Furthermore, pantograph wear depends on the length of the route and the electrical current density J through the pantograph's contact strip.

[0061] Instead of simply storing the aforementioned parameters and wear values, in the exemplary embodiment illustrated in FIG 3, an artificial neural network KN is trained in step 3.II. The aforementioned parameters P, such as the electrical current density J through the contact strip, the speed of the rail vehicle, the pantograph type, the train weight, weather data, the length of the route, etc., are used as input data, which are labeled with the measured output data, i.e. the associated wear values ​​of the wear profile A(P). The artificial neural network is trained in such a way that it determines the associated output data on the basis of the input data. In step 3.III, the trained artificial neural network KN is used to forecast future wear behavior W of a contact strip of a pantograph of a rail vehicle with a fixed timetable FP.

[0062] FIG 4 shows a flow chart 400 which illustrates a method for planning the deployment of rail vehicles according to an embodiment of the invention.

[0063] In step 4.1, the method already illustrated in FIGS. 1 to 3 for estimating a future wear behavior W of a plurality of available rail vehicles for different deployment scenarios of the rail vehicles to fulfill a predetermined timetable FP is carried out.

[0064] In step 4. II, a deployment scenario ES is selected depending on a constraint NB related to the expected future wear W and the required maintenance of the rail vehicles. Such a constraint may, for example, require minimal maintenance effort for the rail vehicles and compliance with the timetable by the rail vehicles.

[0065] FIG. 5 shows an estimation device 50 according to an exemplary embodiment of the invention. The estimation device 50 comprises a database 51 for determining and storing a digital twin ZW of a technical component. The digital twin ZW comprises a current wear status VS and a route-dependent wear profile V for a technical component, in particular a contact strip, of a rail vehicle.

[0066] In addition, the estimation device 50 comprises an estimation unit 52 for estimating the future time-dependent wear behavior W of the rail vehicle as a function of a future timetable FP of the rail vehicle, of the current wear status VS and of the route-dependent wear profile V of the rail vehicle in question.

[0067] FIG. 6 shows a planning device 60 according to an exemplary embodiment of the invention. The planning device 60 comprises the estimation device 50 shown in FIG. 5. Part of the planning device 60 is also a selection unit 61 for selecting an application scenario ES depending on a secondary condition NB relating to the wear and tear and the necessary maintenance of the available rail vehicles. The selection unit 61 queries the estimation device 50 for the digital twins ZW of the individual rail vehicles of an available fleet FUP. The digital twins ZW comprise a current wear status VS of one or more technical components of the individual rail vehicles of the fleet FUP and the respective wear profile V of each rail vehicle.Based on the digital twins ZW of the individual rail vehicles 1 of the FUP fleet, a timetable FP, and a predefined constraint NB, which, in addition to the main condition that no rail vehicle may be used beyond its wear limit, specifies a further optimization goal, the selection unit 61 determines a deployment plan or deployment scenario ES for optimal deployment of the FUP fleet. The constraint NB can, for example, be that minimal maintenance effort should be required.

[0068] Finally, it is pointed out once again that the methods and devices described above are merely preferred embodiments of the invention and that the invention can be varied by those skilled in the art without departing from the scope of the invention, insofar as it is defined by the claims. For the sake of completeness, it is also pointed out that the use of the indefinite articles "a" or "an" does not exclude the possibility that the features in question may be present in multiple copies. Likewise, the term "unit" does not exclude the possibility that it consists of several components, which may also be spatially distributed.

Claims

Patent claims 1 . Method for determining a time-dependent wear behavior (W) of a technical component of a rail vehicle, comprising the steps: - Determination and storage of a digital twin (ZW) of the technical component with a current wear status (VS) and a route-dependent wear profile (V) of the technical component, - Estimation of the future time-dependent wear behavior (W) of the technical component of the rail vehicle depending on a future timetable (EP) of the rail vehicle and based on the digital twin (ZW).

2. Method according to claim 1, wherein the technical component comprises one of the following component types: - a sanding strip, - a wheel profile .

3. Method according to claim 1 or 2, wherein the estimation of the future time-dependent wear behavior (W) of the technical component of the rail vehicle is carried out on the basis of the current wear status (VS) and the route-dependent wear profile (V) of the rail vehicle.

4. Method according to one of the preceding claims, wherein a digital rail network map is used to determine the wear profile (V), to which location-dependent attributes which influence the wear behavior of the technical component are assigned.

5. Method according to claim 4, wherein the digital rail network map depicts routes as graphs with edges as connections between adjacent stations.

6. The method according to claim 5, wherein a distance-related wear profile component (VK) is assigned to each edge of the graph.

7. The method according to claim 6, wherein the route-related wear profile component (VK) comprises at least one of the following data types: - statistics on the wear and tear of the technical component, - weather data, - Vehicle data.

8. A method for planning the deployment of rail vehicles, comprising the steps: - Estimating a future wear behavior (W) of a plurality of available rail vehicles for different deployment scenarios (ES) of the rail vehicles to fulfill a predetermined timetable (FP) using the method according to one of claims 1 to 7, - Selection of an application scenario (ES) depending on a constraint (NB) related to the wear behavior (W) and the necessary maintenance of the rail vehicles.

9. The method according to claim 8, wherein the constraint (NB) comprises the maximum maintenance capacities for the rail vehicles.

10. The method according to claim 8 or 9, wherein the constraint (NB) comprises fulfilling the schedule (FP) with minimal maintenance effort.

11. Method according to one of claims 8 to 10, wherein the secondary condition (NB) comprises fulfilling the timetable (FP) with the smallest possible number of rail vehicles.

12. Estimating device (50) comprising: - a database (51) for determining and storing a digital twin (ZW) of a technical component of a Rail vehicle with a current wear status (VS) and a route-dependent wear profile (V) of the technical component, - an estimation unit (52) for estimating the future time-dependent wear behavior (W) of the rail vehicle as a function of a future timetable (FP) of the rail vehicle and on the basis of the digital twin (ZW).

13. Planning device (60) comprising: - an estimation device (50) according to claim 12 for estimating a future wear behavior (W) of a plurality of available rail vehicles for different deployment scenarios (ES) of the rail vehicles to fulfill a predetermined timetable (FP), - a selection unit (61) for selecting an application scenario (ES) depending on a secondary condition (NB) related to the wear behavior (W) and the necessary maintenance.

14. A computer program product comprising a computer program which is directly loadable into a storage unit, comprising program sections for carrying out the steps of a method according to any one of claims 1 to 11 when the computer program is executed.

15. A computer-readable medium on which program sections executable by a computer unit are stored in order to carry out the steps of a method according to one of claims 1 to 11 when the program sections are executed by the computer unit.

Images