MONITORING, PREDICTING AND MAINTAINING THE CONDITION OF RAILROAD ELEMENTS WITH DIGITAL TWINS

Abstract

The disclosure is directed to a method. The method comprises a data storing step that comprises storing data relating to a represented railway infrastructure system by a data processing system. The method further comprises a condition monitoring step that comprises estimating at least one condition of the represented railway infrastructure system at least by evaluating a set of monitoring models by the data processing system. The method further comprises a predicting step that comprises predicting at least one condition of said represented railway infrastructure system at least by evaluating a set of prediction models by the data processing system, and at least one model evaluation step that comprises evaluating at least one model of at least one condition of at least a portion of the represented railway infrastructure system by the data processing system. The represented railway infrastructure system comprises at least one component and at least one asset. The disclosure is further directed to a corresponding system and a corresponding computer program product.

Claims

1. A method, wherein the method comprises a data storing step that comprises storing data relating to a represented railway infrastructure system by a data processing system, a condition monitoring step that comprises estimating at least one condition of the represented railway infrastructure system least by evaluating a set of monitoring models by the data processing system, a predicting step that comprises predicting at least one condition of said represented railway infrastructure system at least by evaluating a set of prediction models by the data processing system, and at least one model evaluation step that comprises evaluating at least one model of at least one condition of at least a portion of the represented railway infrastructure system by the data processing system, and wherein the represented railway infrastructure system comprises at least one component at least one asset.

2. The method according to claim 1, wherein the method comprises furthermore an optimisation step that comprises at least one of analysing and recommending at least one of inspection activities and maintenance activities for the represented railway infrastructure system at least by evaluating a set of optimisation models by the data processing system.

3. The method according to claim 1, wherein the represented railway infrastructure system comprises furthermore at least one network.

4. The method according to claim 1, wherein the data storing step comprises furthermore at least one of storing sensed data that relate to at least one element of the represented railway infrastructure system, storing load data that relate to a load of at least one element of the represented railway infrastructure system, storing environment data that relate to at least one property of an environment of at least one element of the represented railway infrastructure system, and a data processing step that comprises filtering data.

5. The method according to claim 1, wherein the data storing step comprises furthermore at least one of storing maintenance data that relate to performed and possible maintenance activities of at least one element of the represented railway infrastructure system, and storing inspection data that relate to performed and possible inspection of at least one element of the represented railway infrastructure system.

6. The method according to claim 1, wherein the condition monitoring step comprises at least one of the at least one model evaluation step and at least one of a component condition monitoring step that comprises estimating at least one condition of at least one of the at least one component of the represented railway infrastructure system, an asset condition monitoring step that comprises estimating at least one condition of at least one of the at least one asset of the represented railway infrastructure system, and a network condition monitoring step that comprises estimating at least one condition of at least one of the at least one network of the represented railway infrastructure system.

7. The method according to claim 1, wherein the predicting step comprises at least one of the at least one model evaluation step and evaluating a prediction model of the set of prediction models representing the development of a quantity relating to a degradation of an element in future, wherein said prediction model represents the development at at least one point of time in future.

8. The method according to claim 1, wherein at least one model of the set of prediction models that represents a condition of an element of the represented railway infrastructure system is obtained from or updated with data relating to said condition of the element, at least one model of the set of prediction models that represents a condition of an element of the represented railway infrastructure system is obtained from or updated with data relating to a corresponding condition of at least one other element of a corresponding type, and/or predicting for at least one element of the represented railway infrastructure system at least one of a degradation, a type of the degradation, a severity of the degradation, a type of a failure, a presence of an anomaly, a remaining useful lifetime, a performance and a probability of a failure, and thus generating prediction information for the at least one element.

9. The method according to claim 1, wherein the predicting step comprises at least one of a component predicting step that comprises predicting at least one condition of at least one of the component(s) of the represented railway infrastructure system, an asset predicting step that comprises predicting at least one condition of at least one of the asset(s) of the represented railway infrastructure system, and a network predicting step that comprises predicting at least one condition of at least one of the network(s) of the represented railway infrastructure system.

10. The method according to claim 9, wherein the component predicting step comprises evaluating at least one model from the set of prediction models, wherein the at least one model represents a future development of at least one of the at least one condition of the component as a function of data of at least one data type selected from sensed data, load data, environment data, maintenance data, and specification data, wherein the data of the at least one data type relate to the component or to an asset that comprises the component.

11. The method according to claim 9, wherein the network predicting step comprises at least one of predicting an availability of at least one route in the at least one network at a future point in time, and predicting a capacity of at least one route in the at least one network at a future point in time.

12. The method according to claim 9, wherein the predicting step comprises predicting the condition of at least two elements of the network, and wherein the network predicting step comprises combining the predicted conditions of the at least two elements with the topology and/or operating rules of the network.

13. The method according to claim 1, wherein the method comprises at least one of a data quality estimation step that comprises estimating at least one quality of data regarding the represented railway infrastructure system by the data processing system, and a model validity estimation step that comprises estimating at least one validity of at least one result of evaluating at least one model of the set(s) of models relating to the represented railway infrastructure system by the data processing system.

14. The method according to claim 13, wherein the optimisation step comprises estimating for each of a plurality of combinations of activities from the at least one of the inspection activities and maintenance activities at least one possible outcome, and selecting at least one combination of activities from the plurality of combinations of activities based on an optimization criterion.

15. A system comprising at least one data processing apparatus and at least one sensor configured to sense data relating to a represented railway infrastructure system or a portion thereof, wherein the system is configured to carry out the method steps according to claim 1.

16. A computer program product comprising instructions, which, when the program is executed by the data processing system, cause the data processing system to perform the method steps according to claim 1.

Description

FIGURES

[0433] FIG. 1 shows a represented railway infrastructure system and its relation to other railway infrastructure systems.

[0434] FIG. 2 shows the represented railway infrastructure system and a data storing step.

[0435] FIG. 3 shows an example of a component, sensed data and estimating a condition.

[0436] FIG. 4 shows an example of a result of a condition monitoring step.

[0437] FIG. 5 shows an example of a result of a predicting step.

[0438] FIG. 6 shows some steps and input data of the method.

[0439] FIG. 7 shows possible details of possible input data.

[0440] FIG. 8 shows an example of a feature extraction step.

[0441] FIG. 9 shows an example of a condition of an element.

[0442] FIG. 10 shows an example of representation and/or processing of maintenance events by the method.

[0443] FIG. 11 shows an example of a data-processing system.

[0444] FIG. 1 shows a represented railway infrastructure system 1 that comprises a network 2 comprising assets 3 that each comprise at least one component 4. The number of the assets as well as the number of the components that the assets respectively comprise is merely exemplary. The network comprises furthermore connections or routes between the assets. Those routes can for example correspond to railway connections within the network of the represented railway infrastructure system 1. FIG. 1 shows furthermore two other railway infrastructure systems 101 that also comprise assets 3 which each comprise at least one component 4. For the sake of an example, taking the number of components 4 per asset 3 as indicator for a type of the respective asset 3, at least some of the other railway infrastructure systems 101 comprise assets 3 of a same type as in the represented railway infrastructure system 1. The type of assets 3 that the railway infrastructure systems have in common does not need to be the same for each pair of railway infrastructure systems. That is, the represented railway infrastructure system 1 and a first other railway infrastructure system 101 can both comprise same assets, such as switches, of a same type A. The represented railway infrastructure system 1 and a second railway infrastructure system 101 can both comprise same assets of a different type, such as rails of a type B. The same consideration is applicable for components 4 of assets 3. The same type A or B is not limited to a model name of an asset, but it can also refer to a technical type, and it can be still more precise, e.g. if type A refers to a version of switches that are in use for 10 years and type B refers to the same type of switches, wherein those where mounted later, so that they are for example only in use for 5 years.

[0445] FIG. 2 shows an example of a step of sensing data and a step of storing sensed data relating to the represented railway infrastructure system 1. A method comprises sensing data relating to the represented railway infrastructure system 1, or more particularly to at least one of its assets and/or components. The data are transmitted and at least temporarily stored. The same method or method step can be performed for at least one of the other railway infrastructure systems 101.

[0446] FIG. 3 shows an example of an asset 3 with at least one sensor 20. The position of the at least one sensor 20 is to be understood as an example for sensor(s) 20 capturing data relating to the asset 3 or one or more components 4 thereof. In FIG. 3, the at least one sensor 20 is one acceleration sensor. In this specific example, two components 4 of the asset 3 are rails, and one component 4 is a sleeper. Furthermore, a trackbed under the sleeper is shown as further example of a further component 4.

[0447] FIG. 4 shows an example of a result of a data storing step for sensed data 21 from an asset 3 or a component 4. The data storing step comprises storing timestamped acceleration data at least temporarily as an example for sensed data 21 from a switch as an example for an asset 3 or a component 4. Independently from the types of the sensed data 21 and the asset 3 or a portion thereof to which the sensed data 21 relate, the sensed data 21 can then be processed, e.g. an average, minimum and maximum value can be calculated for intervals of time, such as days or hours, and can be stored and/or used for subsequent processing steps.

[0448] FIG. 5 shows an example of a result of a condition monitoring step and a predicting step for trackbed conditions as an example, wherein the trackbed is a component 4 of an asset 3. A health of the trackbed is monitored through a vertical displacement of a sleeper under passing trains, which reflects how well the sleeper is supported by the trackbed. (An unhealthy trackbed provides a poor support which leads to higher vertical displacement and/or to a higher deflection in general). The condition monitoring step can comprise the above-mentioned data storing step as well as a data processing step and a time-domain analysis of time-series data. FIG. 5 shows a plot of processed data from the data processing step (mean daily vertical displacement observed on a selected asset and a measurement location). Furthermore, the plot shows a prediction of limits of an evolution of said vertical displacement in future. The prediction can be performed by estimating an interval representing a lower and upper estimate of the mean daily displacement in future. Said prediction can be performed for a fixed period, such as each day in a period of 90 days after the last data relating to the trackbed were recorded.

[0449] FIG. 6 shows an example of the method. The method can be an at least partially computer-implemented method. In FIG. 6, the method comprises a data storing step, a condition monitoring step, a predicting step and an optimisation step. The method can comprise further steps, such as a data quality estimation step and/or a model validity estimation step. The data storing step can comprise storing sensed data 21 from at least one sensor 20, and optionally at least one of load data 22, environment data 23, maintenance data 24 and inspection data 25. The data storing step can further comprise storing specification data 26. Each of the condition monitoring step, the predicting step and/or the optimisation step can use at least a part of the data that is stored in the data storing step. The condition monitoring step can comprise evaluating a set of monitoring models 11. This can be performed in a model evaluation step. The part of the stored data that is used in the condition monitoring step can be corresponding at least to input values of the set of monitoring models. The condition monitoring step can also use further data, such as data from another method step, such as from the data quality estimation step.

[0450] Analogously, the predicting step can comprise evaluating a set of prediction models 12, wherein the set of prediction models can optionally be evaluated in a model evaluation step that the predicting step can comprise. Respectively, the part of the stored data that is used can correspond at least to input data of the set of prediction models 12. Furthermore, results from the condition monitoring step or other method steps can optionally be used in the predicting step, for example as supplementary input data for the set of prediction models.

[0451] The optimisation step can comprise evaluating a set of optimisation models 13, wherein the set of optimisation models 13 can optionally be evaluated in a model evaluation step that the optimisation step comprises. The part of the stored data that is used can correspond to input data of the set of optimisation models 13. Results of the condition monitoring step and/or the predicting step as well as of other method steps can optionally also be used in the optimisation step. At least some or all of the method steps can be performed by a data processing system. In particular, the model evaluation steps and/or a transmission of data, such as sensed or stored data, can be performed by the data processing system. However, inspection 25 data and/or maintenance data 24 can optionally be manually or automatically inputted.

[0452] Optionally, an effect of the condition monitoring step can be an estimation of at least one condition of an element 5 of a railway infrastructure system 1 without human inspection or with less human inspection. An optional effect of the predicting step can be an estimation of when an element will fail, that is, of its remaining useful lifetime. Such an information can be optionally advantageous for a maintenance engineer in a maintenance decision. An optional effect of the optimisation step can be an optimised recommendation of maintenance and/or inspection activities that lead to less needed resources and/or lower negative impacts on reliability, availability and/or performance of the represented railway infrastructure system.

[0453] The method can furthermore comprise a part that is not computer-implemented, for example performing inspection activities and/or maintenance activities according to a result of the optimisation step.

[0454] At least one, a plurality or all of the sets of models 10, 11, 12, 13 can be generated by an engineer and/or another person skilled in the art. They can be input data of the method. At least the generation of at least one models can also be at least partially automatically. At least some steps of the generation of at least one model can optionally be automated, such as an integration into the method. The model generation step can also be a part of the method.

[0455] FIG. 6 shows an example of the data storing step, a model generation step and the condition monitoring step comprising a model evaluation step, all of them performed for a degradation of a frog as example of a condition of a component 4 or a part thereof. The degradation can for example be a degradation of a profile of the frog, such as wear or plastic deformation. The degradation can for example also be surface fatigue degradation, such as head checks. The data storing step can optionally comprise storing acceleration data relating to the frog (or another component 4 respectively). Optionally, sensed environment data 23 can be stored in the data storing step. Said environment data can be weather data, such as measures of temperature, humidity and precipitation, as shown in FIG. 7. As stated above, the frog is as an example of a component 4 of an asset 3, wherein the asset 3 can be a railway switch.

[0456] The model generation step can comprise using the stored acceleration data that are relating to the frog as an example of a component 4 of the railway infrastructure system 1. The stored data can for example be interpreted as time-series. Multiple features can be extracted performing time- and frequency domain analyses of the time series. At least one set of models 10 that comprises at least one model is then generated. In this example, a set of monitoring models 11 is generated, wherein this set of monitoring models 11 comprises a machine learning model that is trained to estimate a health indicator of the frog. The health indicator can be a function of the extracted features. The health indicator can be a unitless measure. The health indicator is an indicator for a health condition of the frog, for example having the value 1 when the respective element is brand-new and 0 when it failed. The health indicator can be a part of a condition 30 of the frog or it can be interpreted as an (overall) condition of the frog. That is, the health condition can be a condition 30 of the respective component 4 or asset 3, or at least a part of such a condition, for example a part of a condition of an asset 3, such as a switch, that comprises a component 4, such as the frog.

[0457] The condition monitoring step comprises monitoring at least said condition 30 of said component 4 based on the set of models generated during the model generation step. That is, the sensed data 21 and further data 22, 23, 24, 25, such as the environment data 23 in this example, are used to evaluate the model(s) in the set of monitoring models 11. A result of evaluating the set of monitoring models 11 is an estimation of at least one condition of said component 4, in this case the health indicator. The condition monitoring step can optionally further comprise a post-processing, combining, agglomerating and/or analysing of the estimated condition(s). In this example, the health indicator can furthermore be transformed to a health status indicating an overall state of the component 4 or a respective asset 3 on a discrete scale, for example when conditions or data from several components 4 are agglomerated.

[0458] FIGS. 7, 8 and 9 illustrate the condition monitoring and data storing step for the frog as example of a component of an asset.

[0459] FIG. 7 shows examples of input data, comprising the sensed data 21, such as the acceleration signal, and/or the environment data 23, such as air humidity, temperature and/or precipitation.

[0460] FIG. 8 illustrates an optional embodiment of one of the model generation steps, comprising feature engineering, that is, extracting features from at least a portion of the input data. In this example, the sensed data 21 are again comprising the acceleration data. This model generation step comprises analysing an acceleration signal corresponding to the acceleration data in at least one of the time-domain and the frequency-domain.

[0461] In the time-domain, features such as RMS (Root Mean Squared), minimum, maximum and/or different quantiles are extracted from the acceleration signal. In the frequency domain, an energy of the acceleration signal in different frequency bands is calculated. The features can then be used as input of data or for the generation of a machine learning model and/or an artificial intelligence model belonging to at least one of the sets of models 10, such as the set of monitoring models 11. An output of the model can be the health indicator.

[0462] FIG. 9 details an example of the health indicator. The health indicator can be a continuous variable, which represents the health of a monitored element 5, in this case of said abovementioned component 4, the frog. A certain defined value of the health indicator represents a health and/or a degradation of the component that is considered to be inacceptable, e.g. safety critical, and has impact on the availability of said element and/or an 3 asset to which said element belongs, e.g. said switch to which the frog belongs. From the health indicator, there can furthermore a health status be derived. The health status can be a categorical (discrete) variable that represents the health of the component, in this case said frog. The health status can for example take three categorical values associated to different colours. An optional advantage can be a better perception by the user.

[0463] FIG. 10 shows a prediction of the health of the track bed, as example for the health of a component 4 or an asset 3, after a maintenance event, in this case after tamping of the track bed.

[0464] The upper diagram shows selected historic data that are used for generating a prediction model of the set of prediction models 12. Said selected historic data demonstrate a normalised effect of tamping on the vertical displacement, which can be used as health indicator of the track bed and/or of at least one of its conditions.

[0465] The lower diagram shows a result of a prediction by a model of the set of prediction models 12, in this case by a Bayesian model that was trained with the historic data that are shown in the upper diagram. In the lower diagram, a mean prediction of said model of the set of prediction models 12 is indicated by a solid, non-vertical line. Confidence bounds that represent an uncertainty of such a prediction are indicated by dashed lines in the same diagram.

[0466] A dotted line and lines indicated by crosses in the lower diagram show a prediction which is generated from data from the day when the tamping occurs. (The tamping can also be detected at a later point in time if the data of the day when the tamping occurs are only processed at said later point in time.) The tamping can optionally be detected by at least one of external maintenance data, such as the maintenance data 24, and the sensed data 21. The vertical solid line shows how the prediction is updated with some, for example 5 days of data and/or measurements after the tamping event. An optional advantage of said updating is that with the new data and/or measurements, the uncertainty of the prediction can be significantly reduced.

[0467] FIG. 11 provides a schematic of a data-processing system 200. This data-processing system 200 can be part of a data-processing system or can constitute the data-processing system.

[0468] The data-processing system 200 may comprise a computing unit 135, a first data storage unit 130A, a second data storage unit 130B and a third data storage unit 130C.

[0469] The data-processing system 200 can be a single data-processing system or an assembly of data-processing systems. The data-processing system 200 can be locally arranged or remotely, such as a cloud solution.

[0470] On the different data storage units 130, different data can be stored.

[0471] Additional data storage can be also provided and/or the ones mentioned before can be combined at least in part.

[0472] The computing unit 135 can access the first data storage unit 130A, the second data storage unit 130B and the third data storage unit 130C through the internal communication channel 160, which can comprise a bus connection 160.

[0473] The computing unit 130 may be single processor or a plurality of processors, and may be, but not limited to, a CPU (central processing unit), GPU (graphical processing unit), DSP (digital signal processor), APU (accelerator processing unit), ASIC (application-specific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (field programable gate array). The first data storage unit 130A may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).

[0474] The second data storage unit 130B may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).

[0475] The third data storage unit 130C may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).

[0476] The data-processing system 200 may comprise a further memory component 140 which may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The memory component 140 may also be connected with the other components of the data-processing system 200 (such as the computing component 135) through the internal communication channel 160.

[0477] In addition, the data-processing system 200 may comprise an input user interface 110 which can allow the user of the data-processing system 200 to provide at least one input (e.g. instruction) to the data-processing system 200. For example, the input user interface 110 may comprise a button, keyboard, trackpad, mouse, touchscreen, joystick and the like.

[0478] Additionally, still, the data-processing system 200 may comprise an output user interface 120 which can allow the data-processing system 200 to provide indications to the user. For example, the output user interface 110 may be a LED, a display, a speaker and the like.

[0479] The output and the input user interface 110 may also be connected through the internal communication component 160 with the internal component of the device 200.

[0480] The processor may be singular or plural, and may be, but not limited to, a CPU, GPU, DSP, APU, or FPGA. The memory may be singular or plural, and may be, but not limited to, being volatile or non-volatile, such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F- RAM, or P-RAM.

[0481] The data processing device can comprise means of data processing, such as, processor units, hardware accelerators and/or microcontrollers. The data processing device 20 can comprise memory components, such as, main memory (e.g. RAM), cache memory (e.g. SRAM) and/or secondary memory (e.g. HDD, SDD). The data processing device can comprise busses configured to facilitate data exchange between components of the data processing device, such as, the communication between the memory components and the processing components. The data processing device can comprise network interface cards that can be configured to connect the data processing device to a network, such as, to the Internet. The data processing device can comprise user interfaces, such as: [0482] output user interface, such as: [0483] screens or monitors configured to display visual data (e.g. displaying graphical user interfaces of the questionnaire to the user), [0484] speakers configured to communicate audio data (e.g. playing audio data to the user), [0485] input user interface, such as: [0486] camera configured to capture visual data (e.g. capturing images and/or videos of the user), [0487] microphone configured to capture audio data (e.g. recording audio from the user), [0488] keyboard configured to allow the insertion of text and/or other keyboard commands (e.g. allowing the user to enter text data and/or other keyboard commands by having the user type on the keyboard) and/or trackpad, mouse, touchscreen, joystick — configured to facilitate the navigation through different graphical user interfaces of the questionnaire.

[0489] The data processing device can be a processing unit configured to carry out instructions of a program. The data processing device can be a system-on-chip comprising processing units, memory components and busses. The data processing device can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer. The data processing device can be a server, either local and/or remote. The data processing device can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and/or user interface (such as the upper-mentioned user interfaces).

Numbered References

[0490] 1 represented railway infrastructure system [0491] 2 network [0492] 3 asset [0493] 4 component [0494] 5 element [0495] 10 set of models [0496] 11 set of monitoring models [0497] 12 set of prediction models [0498] 13 set of optimisation models [0499] 20 sensor [0500] 21 sensed data [0501] 22 load data [0502] 23 environment data [0503] 24 maintenance data [0504] 25 inspection data [0505] 26 specification data [0506] 30 condition [0507] 101 other railway infrastructure system