Planning of maintenance of railway

Abstract

The present invention relates to a method for automatically planning maintenance in railway, the method comprising the steps of determining maintenance for different assets at different locations comprising determining at least one of a predicted technical condition of an asset and automatically optimizing the planning accordingly. Further, a railway planning system for automatically planning maintenance comprising a determining component for determining maintenance for different assets at different locations comprising a determining component for determining at least one of a predicted technical condition of an asset and an optimization component for automatically optimizing the planning accordingly.

Claims

1. A method for automatically planning maintenance in railway infrastructure, the method comprising the steps of: (a) determining maintenance for different assets at different locations comprising determining a predicted technical condition of an asset and (b) automatically optimizing the planning accordingly, wherein the optimizing of the planning is based on a degrading effect of a train, maintenance effectiveness metrics and current or predicted traffic information of trains; wherein the optimizing of the planning is based on at least one analytical approach; wherein each analytical approach comprises at least one of signal filter processing pattern recognition, probabilistic modelling, Bayesian schemes, machine learning, supervised learning, unsupervised learning and reinforcement learning, and wherein the asset whose technical condition is predicted comprises one or more switches.

2. The method according to claim 1 with the further step of optimizing the planning based on at least one of the following current or predicted criteria: a technical condition of an asset; traffic load information of trains; and weather information.

3. The method according to claim 1 wherein the determining of maintenance for different assets of a railway infrastructure is based on information gathered from signals from sensors.

4. The method according to claim 3 wherein the sensors are associated with or arranged on/at/in at least one of an asset of railway infrastructure and/or a rolling stock.

5. The method according to claim 1 wherein the signals gathered from the sensor provide information related to acceleration.

6. The method according to claim 1 with the further step of optimizing the planning based on at least one of the following current or predicted criteria: asset life cycle; a geophysical location; an operational importance of an asset; a time of the maintenance measure; a complexity of the maintenance measure; a cost of the maintenance measure; stock of replacement parts used for the maintenance measure; a safety measure necessary for the maintenance measure; budget information; staff availability; maintenance vehicle availability; and tool availability.

7. The method according to claim 1 further comprising the step of involving different servers for at least two of the determining maintenance for current technical conditions, determining maintenance of predicted technical conditions and for automatically optimizing the planning.

8. The method according to claim 1 further comprising the step of automatically controlling the maintenance planning.

9. The method according to claim 1 further comprising the step of changing current maintenance planning according to renewed optimization.

10. The method according to claim 1 further comprising the step of providing and receiving feedback of current maintenance measures.

11. A railway planning system for automatically planning maintenance comprising (a) a determining component for determining maintenance for different assets at different locations comprising a determining component for determining a predicted technical condition of an asset and (b) an optimization component for automatically optimizing the planning accordingly, wherein the optimization component performs the optimizing of the planning based on a degrading effect of a train, maintenance effectiveness metrics and current or predicted traffic information of trains; wherein the optimizing of the planning is based on at least one analytical approach; wherein the information gathered at least from one sensor is processed by an analyzing component comprising at least one analytical approach; wherein each analytical approach comprises at least one of the signal filter processing pattern recognition, probabilistic modelling, Bayesian schemes, machine learning, supervised learning, unsupervised learning and reinforcement learning; and wherein the asset whose technical condition is predicted comprises one or more switches.

12. The system according to claim 11 with the further component for optimizing the planning based on at least one of the following current or predicted criteria: a technical condition of an asset; traffic load information of trains; and weather information.

13. The system according to claim 11 further comprising sensors at different geophysical locations wherein the determining component for determining maintenance for different assets is configured to provide information gathered from signals from the sensors.

14. The system according to claim 11 wherein the sensors are associated with or arranged at least on/or/at one of a railway infrastructure and/or rolling stock.

15. The system according to claim 11 wherein the signals gathered from the sensor provide information of acceleration.

16. The system according to claim 11 with the further component for optimizing the planning based on at least one of the following current or predicted criteria: asset life cycle; a geophysical location; an operational importance of an asset; a time of the maintenance measure; a complexity of the maintenance measure; a cost of the maintenance measure; stock of replacement parts used for the maintenance measure; a safety measure necessary for the maintenance measure; budget information; staff availability; maintenance vehicle availability; and tool availability.

17. The system according to claim 11 further comprising different servers for at least two of the component for determining maintenance for current technical conditions, the component for determining maintenance of predicted technical conditions and the component for automatically optimizing the planning.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 depicts an example of a set-up of several sensors to a railway infrastructure in accordance with the present invention;

(2) FIG. 2 depicts an example of the set-up of the sensors according to FIG. 1 and associated infrastructure in accordance with the present invention;

(3) FIG. 3 depicts a portion of a railway infrastructure with various dislocation of sensors and different available maintenance options.

EMBODIMENTS

(4) Below, maintenance method embodiments will be discussed. The letter M followed by a number abbreviates the method embodiments. Whenever reference is herein made to method embodiments, these embodiments are meant.

Method

(5) M01: A method for automatically planning maintenance in railway, the method comprising the steps of determining maintenance for different assets at different locations comprising determining at least one of a predicted technical condition of an asset and automatically optimizing the planning accordingly.

(6) M02: The method according to the preceding embodiment with the further step of optimizing the planning based on at least one of the following current or predicted criteria: a. a technical condition of an asset; b. a degrading effect of a train; c. traffic load information of trains; d. maintenance effectiveness metrics; and e. weather information.

(7) M03: The method according to the preceding embodiment wherein the determining of maintenance for different assets is based on information gathered from signals from sensors.

(8) M04: The method according to the preceding embodiment wherein the information gathered at least from one sensor is based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning and/or reinforcement learning.

(9) M05: The method according to any of the preceding two embodiments wherein the sensors are associated with or arranged at least one of rolling stock, a sleeper, a frog, a point machine, the rail frog, a rail blade and/or an interlocking for particularly measuring current at the interlocking.

(10) M06: The method according to any of the preceding embodiments wherein the signals gathered from the sensor provide information of at least one of temperature, acceleration, vibration, ultra-sound, time, distance, current, pressure, movement, humidity, precipitation and/or acoustics.

(11) M07: The method according to any of the preceding embodiments wherein the planning optimizing is based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning and/or reinforcement learning.

(12) M08: The method according to the preceding embodiment with the further step of optimizing the planning based on at least one of the following current or predicted criteria: a. asset life cycle; b. a geophysical location; c. an operational importance of an asset; d. a time of the maintenance measure; e. a complexity of the maintenance measure; f. a cost of the maintenance measure; g. traffic information of trains; h. stock of replacement parts used for the maintenance measure; i. a safety measure necessary for the maintenance measure; j. budget information; k. staff availability; l. maintenance vehicle availability; and m. tool availability.

(13) M09: The method according to any of the preceding embodiments further comprising the step of involving different servers for at least two of the determining maintenance for current technical conditions, determining maintenance of predicted technical conditions and for automatically optimizing the planning.

(14) M10: The method according to any of the preceding embodiments further comprising the step of changing current maintenance planning according to renewed optimization.

(15) M11: The method according to any of the preceding embodiments further comprising the step of providing and receiving feedback of current maintenance measures.

(16) M12: The method according to any of the preceding embodiments further comprising the step of automatically controlling the maintenance planning.

(17) Below, system embodiments will be discussed. These embodiments are abbreviated by the letter “S” followed by a number. When reference is herein made to a system embodiment, those embodiments are meant.

System

(18) S01: A railway planning system for automatically planning maintenance comprising a determining component for determining maintenance for different assets at different locations comprising a determining component for determining at least one of a predicted technical condition of an asset and an optimization component for automatically optimizing the planning accordingly.

(19) S02: The system according to the preceding embodiment with the further component for optimizing the planning based on at least one of the following current or predicted criteria: a. a technical condition of an asset; b. a degrading effect of a train; c. traffic load information of trains; d. maintenance effectiveness metrics; and e. weather information.

(20) S03: The system according to any of the preceding system embodiments further comprising sensors at different geophysical locations wherein the determining component for determining maintenance for different assets is configured to provide information gathered from signals from the sensors.

(21) S04: The system according to the preceding system embodiment wherein the information gathered at least from one sensor is processed by an analyzing component comprising at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning and/or reinforcement learning.

(22) S05: The system according to any of the preceding two system embodiments wherein the sensors are associated with or arranged at least to one of railway infrastructure such as a sleeper, a frog, a point machine, the rail frog, a rail blade and/or an interlocking for particularly measuring point machine current at the interlocking.

(23) S06: The system according to any of the preceding system embodiments wherein the signals gathered from the sensor provide information of at least one of temperature, acceleration, vibration, ultra-sound, time, distance, current, pressure, movement, humidity, precipitation and/or acoustics.

(24) S07: The system according to any of the preceding system embodiments wherein the planning component for optimizing is making use of at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning and/or reinforcement learning.

(25) S08: The system according to the preceding system embodiment with the further component for optimizing the planning based on at least one of the following current or predicted criteria: a. asset life cycle; b. a geophysical location; c. an operational importance of an asset; d. a time of the maintenance measure; e. a complexity of the maintenance measure; f. a cost of the maintenance measure; g. traffic information of trains; h. stock of replacement parts used for the maintenance measure; i. a safety measure necessary for the maintenance measure; j. budget information; k. staff availability; l. maintenance vehicle availability; and m. tool availability.

(26) S09: The system according to any of the preceding system embodiments further comprising different servers for at least two of the component for determining maintenance for current technical conditions, the component for determining maintenance of predicted technical conditions and the component for automatically optimizing the planning.

(27) S10: The system according to any of the preceding system embodiments further comprising a component for changing current maintenance planning according to renewed optimization.

(28) S11: The system according to any of the preceding system embodiments further comprising a component for providing and receiving feedback of current maintenance measures.

(29) S12: The system according to any of the preceding system embodiments further comprising a component for automatically controlling the maintenance planning.

(30) Whenever a relative term, such as “about”, “substantially” or “approximately” is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., “substantially straight” should be construed to also include “(exactly) straight”.

(31) Whenever steps are recited in the appended claims, it should be noted that the order in which the steps are recited in this text may be the preferred order, but it may not be mandatory to carry out the steps in the recited order. That is, unless otherwise specified or unless clear to the skilled person, the orders in which steps are recited may not be mandatory. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Y1), . . . , followed by step (Z). Corresponding considerations apply when terms like “after” or “before” are used.

DETAILED DESCRIPTION OF THE FIGURES

(32) FIG. 1 provides a schematic description of a system configured for a railway infrastructure. There is shown an example of a railway section with the railway 1 itself, comprising rails 2 and sleepers 3. Instead of the sleepers 3 also a solid bed for the rails 2 can be provided.

(33) Moreover, a mast 4 is shown that is just one further example of constructional elements that are usually arranged at or in the vicinity of railways. Also a tunnel 5 is shown. It is needless to say that other constructions, buildings etc. can be present and also used for the present invention as described before and below.

(34) A first sensor 10 can be arranged on one or more of the sleepers. The sensor 10 can be an acceleration sensor and/or any other kind of railway specific sensor. Examples have been mentioned before.

(35) A second sensor 11 is also arranged on another sleeper distant from the first sensor 10. Although it seems just a small distance in the present example, those distances can range from the distance to the neighboring sleeper to one or more kilometers. Other sensors can be used for attachment to the sleepers as well. The sensors can further be of different kind—such as where the first sensor 10 may be an acceleration sensor, the second sensor 11 can be a magnetic sensor or any other combination suitable for the specific need. The variety of sensors are enumerated before.

(36) Another kind of sensor 20 can be attached to the mast 4 or any other structure. This could be another sensor, such as an optical, temperature, even acceleration sensor etc. A further kind of sensor 30 can be arranged above the railway as at the beginning or within the tunnel 5. This could be height sensor for determining the height of a train, an optical sensor, a doppler sensor etc. All those sensors mentioned here and before are just non-limiting examples.

(37) FIG. 2 is intended to provide an example for a hardware/software infrastructure that can vary for different needs. Sensors 10 and 11 can be connected to a common component 15, such as a server 15, with the functions like transmitting, storing, resending and/or processing etc.). All sensors 10, 11, 20, 30 could additionally or alternatively be connected to another server or storage 40 that is collecting the data, storing and transmitting it. In the latter case server 15 can be regarded as a pre-processing unit, a data collection unit, a filtering or calibrating unit.

(38) In the example shown, the data is further submitted (pushed and/or pulled) to a remote server 50, a plurality of servers 50, 60, cloud computing, cloud storages etc. regularly or unregularly upon need. These components may be used for more sophisticated computing, as for example used for training a neural network.

(39) Any transmission between the sensors, other components, such as servers etc., can be hard-wired and/or wireless, depending on the needs and the further infrastructure.

(40) All sensors 10, 11, 20, 30 may further be used for traffic control, security reasons, source for billing purposes etc. and the data may further be copied for maintenance purposes, be the purpose predictive, precautionary or due to an exceptional value that may cause immediate or quick reaction by the maintenance team.

(41) Server 200 can be a working server for the maintenance team. In the embodiment, server 200 is connected to server 40 and/or to server 50. Server 200 can be configured to collect further data from the network that may comprise availability information of team members at maintenance entity 100 and/or from a spare parts entity 110.

(42) The sensors may contribute their values to a local network, as explained before, that can be connected wirelessly or wired. Further, local read-outs may be accomplished and initiate a signaling, a forced braking or any other measure. Further, the read-out value can be disregarded, for instance, if the sensor is intended to detect exceptional values only. Also, the server 15 or any other server in the hierarchy or the network (40, 50) may disregard single or a plurality of signals or apply a weighting in the sense of weighing the relevance.

(43) FIG. 3 depicts an exemplary extraction of a real railway network. An irregular condition may have been detected at sensor or device 110; a sensor or device of the same make in this embodiment is found at location 106. Further, a switch or a component of the switch 240 is close to an end to its asset life cycle.

(44) Further, not depicted, a similar sensor or device like the one at 110 or 106 can be at a remote location or even another entity. The method according to the invention can then alert a supervising or remote database about a concern that may arise based on the determination of the reason for a malfunction, should this be based in a manufacturing failure or any other principal failure (wrong application for instance).

(45) The method and system of the invention will determine the necessity to at least check for the reason and the effort to be taken to initiate a repair and/or maintenance measure at sensor or device 110.

(46) The method according to the invention will inform a railway operation administrator that the lane located between location 108 and 112 must be closed for the maintenance down time. This allows the operations coordinator (not depicted) to organize all measures necessary to get a train at station 200 to the main station 100. Although this train would in normal conditions travel along locations 220, 230, 240 further via 108, 110 and 112 to main station 100. However, because the lane passing sensor or device 110 will have to be closed, the operations coordinator may announce and schedule a rerouting via 220, 230, 240, 106, 104, 102, 250 and 260 to the main station 100.

(47) The maintenance planning system may determine that however that switch 240 should also be replaced or applied a maintenance measure to. In such a case, the operation administrator would have to even reroute the train at location 200 via 250 and 260 to the main station 100, at least during the time that switch 240 is inoperative due to maintenance measures.

(48) The maintenance planning scheme may schedule—in dependency of the priority needed—predictively may be included into the maintenance mission sent to sensor or device 110. Sensor or device 108 and 112 may receive a priority for precautionary maintenance measure, because, first, the maintenance resource is anyhow located close to these two sensors or devices. Further, the corresponding lane must be closed anyhow, such the affect on the operations of trains may be less than if the lane would have to be closed down later.

(49) During the transfer of the maintenance resources from workshop 300 to the lane that is closed to the general train traffic to apply maintenance measures to the sensors or devices 108, 110 and 112, the method according to the invention will coordinate with the scheduled train traffic and the operation manager to keep the path for the maintenance machine available from workshop 300 via 220, 230 and 240 to the location of actual activity open.

(50) The method according to the invention will further, after having carried out the maintenance measures at locations 110, 112 and 108, return into the direction of workshop 300, however have in mind that the switch 240 also needs some work to be done. Thus, in coordination with the operations coordinator initiate the closure of the whole part of the infrastructure, here depicted with the numerals 220, 230, 240, further 106, 104 and 102. Note, the portion where sensors or devices 108, 110 and 112 are located, can be released for a pendular traffic of trains between the main station 100 and sensor or device 108 (which could be a small station).

(51) After having repaired the switch 240 or the component of the switch 240 that had to be maintenance, the machine can be sent to sensors 102, 104 and 106 to carry out whatever measures are necessary or precautionary serviced. Note, in this case, the branch from station 200 via 220, 230, 240, 108, 110, 112 to main station 100 can be released to the operations coordinator.

(52) After having completed all work that has been assigned by the inventive method and system, the machine has to return to the workshop, in this embodiment. This return way can be assigned a smaller priority, if no imminent works are scheduled by the maintenance planning system.

(53) The necessity of maintenance measures or their usefulness may be determined via use of machine learning methods like an artificial neural network that can be trained locally and/or remotely. As one result of the train type classification and the prior list of train types the invention calculates the speed and accumulates the vibration energy of the recorded data from a train passage. Such information, that was not available continuously in the state of the art and therefore could not be used for condition monitoring and prediction, can be used as a basis for the decision, where and when maintenance measures are meaningful.

(54) The subject matter of the invention also uses data from multiple sensors at one asset to separate different origins of recorded signals via different signal processing methods or analytical approaches. In this example a train runs over three succeeding sensor systems at one asset and an independent component analysis is used to separate noise from train borne signals and from asset borne signals. Such an information gained from these detections may let the necessity of maintenance measures appear more or less likely. A heavy train obviously can consume more resources than a small train, a trolley may use less resources than a fast-speed train.

(55) The information derived in previous steps can be used to detect anomalies, provide a health condition conclusion, diagnose a failing component, and/or predict a condition development trend.

(56) The boundaries for normal behavior are pre-set, automatically set and/or set via machine learning methods (like by support vector machines). The anomaly lies outside the boundary but it does not resemble known failure. Compared to the state of the art in which such derived models are not possible the invention can reduce uncertainty and enable automated anomaly detection with higher accuracy. The invention can use the information to identify patterns related to failure modes of the ballast or the geometry, here the unsupported sleepers or surface failures of rails. Such pattern is formed by single values that directly reveal a failure or intolerable condition like the certain vertical movement at a certain speed and train type. Alternatively, or additionally, such patterns are present in the frequency and time domain of measured and combined data and transformed via signal processing methods such as Fourier Transformation or Wavelet Transformation. Machine learning classification methods like artificial neural networks are used to identify the class of the defect (here a crack) and/or the component (here the frog) and/or the location (here the tip of the frog). Compared to the state of the art in which dedicated temporal measurement devices are used to execute a certain measurement the invention derives multiple condition assessments from one or multiple sources using one or more ranges of the signals.