EP4565470A1 - Prognostizierung des verschleissverhaltens von schienenfahrzeugen - Google Patents
Prognostizierung des verschleissverhaltens von schienenfahrzeugenInfo
- Publication number
- EP4565470A1 EP4565470A1 EP23797664.2A EP23797664A EP4565470A1 EP 4565470 A1 EP4565470 A1 EP 4565470A1 EP 23797664 A EP23797664 A EP 23797664A EP 4565470 A1 EP4565470 A1 EP 4565470A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- wear
- rail vehicle
- rail
- technical component
- dependent
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0058—On-board optimisation of vehicle or vehicle train operation
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0072—On-board train data handling
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0081—On-board diagnosis or maintenance
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
- B61L27/57—Trackside diagnosis or maintenance, e.g. software upgrades for vehicles or trains, e.g. trackside supervision of train conditions
Definitions
- the invention relates to a method for determining a time-dependent wear behavior of a technical component of a rail vehicle.
- the invention also relates to a method for planning the deployment of rail vehicles.
- the invention also relates to an estimation device.
- the invention also relates to a planning device.
- 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 saved.
- a technical component is to be understood as a technical functional unit of the rail vehicle used in the 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 revised or repaired as part of a maintenance measure.
- a digital twin is to be understood as a digital representation of a technical component that includes information about the physical or technical condition of the technical component. In this specific case, the digital twin represents a current wear status and location-dependent wear behavior of a technical component of a rail vehicle.
- the current wear status indicates the last measured value of wear on the technical component.
- a wear profile is understood to be a location-dependent measure of wear of the technical component.
- the location dependency is preferably implemented by dividing the route network into route sections and determining an extent of wear of the technical component assigned to a respective route section. Since the wear of the technical component on a single trip over a route section is usually very low, the cumulative wear of a rail vehicle is measured after a large number of trips on the route section and, on the basis of the cumulative wear, an average wear of the rail vehicle on a single trip over the route section is calculated.
- a route section is formed by a rail section between two stations.
- the route section is preferably formed by a rail section between two adjacent stations.
- 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 an observation of a large number of rail vehicles and scenarios with different parameters or parameter values. As will be explained in more detail later, the wear of a pantograph or of wheels of 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 is dependent on different different variables and/or parameters that influence the wear of a technical component.
- the future time-dependent wear behavior of the rail vehicle is estimated depending on a future timetable of the rail vehicle and on the basis of the digital twin.
- the digital twin includes both a current wear status of the technical component 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.
- the timetable can be used to determine the future route and route usage of a rail vehicle.
- the route sections assigned to the route can be determined on the basis of the route, and the wear profile values or wear profile functions assigned to the individual route sections and known and predicted values for variables and parameters can be used to calculate predicted wear values for an individual rail vehicle for individual route sections.
- the predicted total wear when a timetable is met in a predetermined time interval is then the sum of the predicted wear values for each route section.
- a maintenance interval or the next maintenance time can be determined based on the predicted wear of a technical component of a rail vehicle.
- Conventionally fixed maintenance intervals can therefore be individually adjusted when 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 according to the timetable.
- Rail vehicles can also be more optimally be used for specific driving missions, taking into account their current wear status and their respective predicted next maintenance time.
- a method for planning the deployment of rail vehicles can also be specified.
- a future wear behavior of a plurality of available rail vehicles for different deployment scenarios of the rail vehicles is determined to meet 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.
- 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 includes one or more wear and maintenance-related criteria to be met.
- An deployment scenario is to be understood as a plan according to which certain vehicles are used specifically on certain routes according to a timetable in order to meet the secondary condition mentioned.
- a criterion can require a minimum amount of maintenance in a predetermined time interval.
- the use of rail vehicles can also be adapted to maintenance capacities.
- the wear and tear of rail vehicles is controlled in such a way that, with a minimum number of rail vehicles to be kept in reserve and with existing or specified maintenance capacities, there is always sufficient maintenance capacity for the rail vehicles, so that there are no timetable failures due to maintenance backlogs and the like.
- the estimation device has a database for determining and storing a digital twin of a technical component of a rail vehicle with an active 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 depending on 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 as well as 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.
- the planning device has an estimation device according to the invention for estimating wear of 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 the necessary maintenance of the rail vehicles.
- the planning device shares the advantages of the method according to the invention for deployment planning of rail vehicles.
- a large part of the above-mentioned components of the estimation device and planning device according to the invention can be implemented in whole or in part 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 previously used computer systems can also be easily replaced by a software ware update in order to work in the manner according to the invention.
- a corresponding computer program product with a computer program which can be loaded directly into a computer system, with program sections in order to carry out 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 planning the deployment 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 deployment 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 can, in addition to the computer program, optionally contain additional components, such as, for example, documentation and/or additional components, including hardware components such as hardware keys (dongles, etc.) for using the software.
- 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 the control unit and/or for storage on or in the computer system or the control unit.
- the computer system can, for example, have one or more cooperating microprocessors or the like for this purpose.
- 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 leads to a large number 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 results in friction effects which lead to wear of the technical component.
- the contact element is particularly preferably part of the stationary railway infrastructure, whereby the technical component moves relative to the contact element.
- the wear of a technical component in direct contact with the infrastructure is usually route-dependent, it is particularly effective to estimate the future time-dependent wear behavior of a technical component of a rail vehicle on the basis of 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.
- 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 in the case of an internal technical component of a rail vehicle, different stresses on the technical component depending on the route can have an individual influence on the extent of its wear.
- the technical component comprises one of the following component types:
- the current wear status for the grinding bar preferably includes information regarding the current state of wear or wear status of the grinding bar. This information preferably includes a current grinding bar thickness.
- 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.
- the wear profile preferably comprises a digital rail network map to which location-dependent attributes are assigned which influence the wear profile.
- location-dependent attributes enable the creation of a "wear map” which allows a prediction 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 gradient or gradient.
- a digital rail network map makes it possible to assign attributes which influence the wear profile to points or sections or route sections.
- Digital maps can be based on map material, for example detailed map material available on the Internet such as "Openrail” or “OpenStreetMap", which each depicts routes as graphs.
- the digital rail network map preferably depicts the routes as graphs with edges as connections between neighboring stations, preferably railway stations. Wear profile components related to route sections are preferably assigned to the edges of the graph. It is advantageous to calculate total wear for a given timetable by adding values of wear profile components related to route sections of different route sections.
- wear patterns can 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 rare 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 cause such an effect.
- Pressure differences generated at tunnel entrances and exits, which generate unwanted transverse forces and thus create their own patterns of wear can also cause instantaneous wear.
- the section-related wear profile component preferably comprises at least one of the following data types:
- an expected value can be calculated for the wear and tear of the technical component that will occur when a predetermined timetable is met.
- Weather data provides information about the current and expected weather conditions when fulfilling a timetable. These weather conditions influence the wear and tear of a technical component of a rail vehicle.
- Vehicle data or vehicle-specific data also influence the expected wear of a technical component of a rail vehicle.
- 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.
- the vehicle data preferably includes at least one of the following data types: - Vehicle characteristics,
- the vehicle characteristics include individual properties of a rail vehicle, for example a vehicle version or a vehicle type.
- Mission data refers to all data that affect the wear and tear of technical components and are related to the individual ferry operations of the individual rail vehicles.
- Mission data includes in particular the routes travelled and their length and condition.
- the maintenance data includes the current wear condition, preferably the grinding strip thickness or wheel rim thickness and, if applicable, also carried out or planned maintenance activities.
- the type of route travelled influences the wear behaviour of various technical components of a rail vehicle. For example, wheels or contact strips wear more on routes with steep gradients than on flat routes. The wear of contact strips and wheels is also greatly increased at higher speeds. The wear of the wheels also depends on the geometry of the wheels and the tracks travelled on.
- wear of a technical component can be represented as a linear or non-linear function of a number of variables and parameters.
- parameters preferably include the length of a track section, the average speed of a rail vehicle on the track and specifically for a grinding bar the current density through the grinding bar.
- a model based on artificial intelligence can also 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.
- labeled training data is used, 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.
- Such a model can advantageously even be retrained or updated during a rail vehicle journey if parameter values and values for variables and wear values of the technical components are measured during or after the journey, so that an increased accuracy and timeliness of the model is achieved.
- wear and tear on a technical component such as a contact strip or a wheel tire
- 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 and tear based on statistics, less data needs to be retained.
- Such a model preferably includes a regressor.
- a regressor can be used to determine a functional relationship based on statistical data.
- the secondary condition preferably includes the maximum maintenance capacities for the rail vehicles.
- ferry operations can advantageously be maintained according to schedule.
- the constraint includes minimal maintenance effort for the rail vehicles.
- resources for maintaining the rail vehicles can be saved.
- the secondary condition also preferably includes fulfilling the timetable without maintenance.
- This variant is intended to avoid vehicles having to be taken out of service for a time in order to maintain them. This approach can be useful if very few rail vehicles are available and maintenance-related downtimes are to be avoided.
- the constraint may also include meeting 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 use at the same time.
- the extent of wear and tear on the technical component is preferably determined automatically.
- the wear and tear twin can advantageously be created without any personnel expenditure.
- FIG 1 is a graph of a train connection illustrating the wear behaviour of a contact strip of a rail vehicle
- FIG 2 is a diagram illustrating a dependence of wear behaviour on a number of parameters
- FIG 3 is a flow chart illustrating an estimation of wear behavior based on an AI-based model
- FIG 4 is a flow chart illustrating a planning method according to an embodiment of the invention.
- FIG 5 is a schematic representation of an estimation device according to an embodiment of the invention.
- FIG 6 is a schematic representation of a planning device according to an embodiment of the invention.
- 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 to B) via Stuttgart (abbreviated to L), Nuremberg (abbreviated to N) to Kunststoff (abbreviated to M).
- wear data V BL , V LN , V NM are shown or arranged as route-related wear profile components VK for sections between the individual cities for a contact strip of a rail vehicle.
- wear data can also include information on the wear of a plurality of contact strips of different rail vehicles.
- the wear data can also include statistics on the wear of a contact strip for a plurality of rail vehicles.
- the wear can usage profile a dependence 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 applicable, 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 wear strip thickness of a wear strip on a specific rail vehicle is known, the aforementioned data can be used to estimate at what point in time the rail vehicle needs to be serviced 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.
- FIG 2 shows a diagram 200 which represents a model or a non-linear function of 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 centimeter cm 2 ) through the contact strip.
- a forecast of wear during a train journey on a predetermined rail route can be determined without having to resort directly to a database with extensive statistics.
- FIG 3 shows a flow chart 300 which illustrates an estimation of a wear behavior of a pantograph based on an AI-based model.
- step 3.1 data regarding the wear profile A (P) of a pantograph type are collected for each section or edge of a graph representing a route map.
- a certain Pantograph type is specified depending on which power system the associated route is equipped with. If, for example, a pantograph of a first type of pantograph has to be used on a section of route, it does not make sense to also display values for a second type of pantograph that is technically different to the first type of pantograph.
- the type of traction current is specified for each section of route, i.e. whether, for example, DC (direct current) or AC (alternating current) is used and the electrical voltage at which the traction current network in this section is operated, e.g.
- pantograph types and different types of traction current can be used by one and the same rail vehicle.
- Other parameters P that influence the wear and tear of a pantograph relate to the speed of the rail vehicle, weather data and the train weight.
- the train weight influences the electrical current density J of the electric current flowing through the pantograph.
- the wear of a pantograph depends on the length of the track and the electrical current density J through the pantograph's contact strip.
- an artificial neural network KN is trained in step 3.II.
- the parameters P mentioned 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.
- the trained artificial neural network KN is used to predict future wear behavior W of a contact strip of a pantograph of a rail vehicle with a fixed timetable FP.
- 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.
- 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 usage scenarios of the rail vehicles to fulfill a predetermined timetable FP is carried out.
- a deployment scenario ES is selected depending on a secondary condition NB related to the expected future wear W and the necessary maintenance of the rail vehicles.
- a secondary condition can, for example, require a minimum maintenance effort for the rail vehicles and compliance with the timetable by the rail vehicles.
- 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.
- 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, the current wear status VS and the route-dependent wear profile V of the rail vehicle in question.
- 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 related 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.
- the selection unit 61 determines a deployment plan or deployment scenario ES for optimal use of the FUP fleet.
- the secondary condition NB can, for example, be that minimal maintenance effort should be required.
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102022211085.4A DE102022211085A1 (de) | 2022-10-19 | 2022-10-19 | Prognostizierung des Verschleißverhaltens von Schienenfahrzeugen |
| PCT/EP2023/077746 WO2024083531A1 (de) | 2022-10-19 | 2023-10-06 | Prognostizierung des verschleissverhaltens von schienenfahrzeugen |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| EP4565470A1 true EP4565470A1 (de) | 2025-06-11 |
Family
ID=88585409
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| EP23797664.2A Pending EP4565470A1 (de) | 2022-10-19 | 2023-10-06 | Prognostizierung des verschleissverhaltens von schienenfahrzeugen |
Country Status (3)
| Country | Link |
|---|---|
| EP (1) | EP4565470A1 (de) |
| DE (1) | DE102022211085A1 (de) |
| WO (1) | WO2024083531A1 (de) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102023212899A1 (de) | 2023-12-18 | 2025-06-18 | Siemens Mobility GmbH | Verfahren zur Bestimmung eines Raddurchmessers bei einem Schienenfahrzeug |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102009024506A1 (de) * | 2009-06-08 | 2010-12-09 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Verfahren zur Ermittlung von Wartungsinformationen |
| DE102016109497A1 (de) | 2016-05-24 | 2017-11-30 | Knorr-Bremse Systeme für Schienenfahrzeuge GmbH | Verfahren und Vorrichtung zum Ermitteln des Verschleißzustandes von Komponenten eines Schienenfahrzeugs |
| ES2887380T3 (es) * | 2017-05-24 | 2021-12-22 | Siemens Mobility GmbH | Control de condición de un elemento de desgaste por uso |
| DE102017217450A1 (de) | 2017-09-29 | 2019-04-04 | Siemens Mobility GmbH | Verfahren zur Zustandsbestimmung von wenigstens einer entlang einer Fahrstrecke verlaufenden Fahrleitung |
| PL3814194T3 (pl) | 2018-08-31 | 2025-01-20 | Siemens Mobility GmbH | Optymalizacja energii przy eksploatowaniu floty pojazdów szynowych |
| CN112633532B (zh) | 2020-12-29 | 2023-06-02 | 上海工程技术大学 | 一种基于数字孪生技术的列车车轮维修管理系统 |
-
2022
- 2022-10-19 DE DE102022211085.4A patent/DE102022211085A1/de not_active Withdrawn
-
2023
- 2023-10-06 WO PCT/EP2023/077746 patent/WO2024083531A1/de not_active Ceased
- 2023-10-06 EP EP23797664.2A patent/EP4565470A1/de active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| DE102022211085A1 (de) | 2024-04-25 |
| WO2024083531A1 (de) | 2024-04-25 |
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