EP3254928A1 - System und verfahren zur anlagenverwaltung von eisenbahnzügen - Google Patents

System und verfahren zur anlagenverwaltung von eisenbahnzügen Download PDF

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Publication number
EP3254928A1
EP3254928A1 EP16174036.0A EP16174036A EP3254928A1 EP 3254928 A1 EP3254928 A1 EP 3254928A1 EP 16174036 A EP16174036 A EP 16174036A EP 3254928 A1 EP3254928 A1 EP 3254928A1
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EP
European Patent Office
Prior art keywords
subsystem
train
autonomous agent
events
autonomous
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
Application number
EP16174036.0A
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English (en)
French (fr)
Inventor
Abdallah Asse
Thierry Berger
Guillaume BRANGER
Joffrey CLARHAUT
Khaled EL SANWAR
Frédéric GRZESIAK
Antoine LE MORTELLEC
Yves SALLEZ
Damien TRENTESAUX
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Alstom Holdings SA
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Bombardier Transportation GmbH
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Publication date
Application filed by Bombardier Transportation GmbH filed Critical Bombardier Transportation GmbH
Priority to EP16174036.0A priority Critical patent/EP3254928A1/de
Priority to PCT/EP2017/062623 priority patent/WO2017211593A1/en
Publication of EP3254928A1 publication Critical patent/EP3254928A1/de
Pending legal-status Critical Current

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    • 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
    • B61L15/00—Indicators provided on the vehicle or train for signalling purposes
    • B61L15/0018—Communication with or on the vehicle or train
    • 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 present disclosure relates to an asset management system and method, more particularly for a diagnostic system and a method for monitoring a fleet of rail vehicles circulating on a rail infrastructure, for identifying particular faults relating to components of the rail vehicles, and for analyzing the health status of components of that fleet.
  • GB 2 392 983 A discloses a remote system condition monitoring, including a diagnostic apparatus and method for monitoring a system including a plurality of monitored components.
  • GB 2 378 248 A discloses a fault detection and prediction system for vehicles, comprising sensors associated with individual components of a plurality of vehicles.
  • EP 1 900 597 B1 describes a diagnostic system for monitoring a rail system, comprising a rail infrastructure and at least one fleet of vehicles circulating thereon.
  • a system for the asset management of railway trains comprises a diagnosis system which includes a train surveillance system comprising sensors; at least one surveillance subsystem comprising sensors; a train autonomous agent, being a computer program; at least one subsystem autonomous agent, being a computer program; at least one communication network; wherein the train surveillance system, the at least one surveillance subsystem, the train autonomous agent and the at least one subsystem autonomous agent are nodes of the at least one communication network and are communicatively connected over the at least one communication network, and wherein the at least one subsystem autonomous agent and the train autonomous agent are adapted to create abstracted information in the form of events, by:
  • a method for conducting prognostic and health monitoring assessment for trains employing a system according to the first aspect, wherein the at least one subsystem autonomous agent collects and filters data from at least one subsystem diagnostic autonomous agent, and cooperates with at least one other subsystem autonomous agent for data comparison, and the train autonomous agent collects and filters data from the train diagnostic autonomous agent, cooperates with a subsystem autonomous agent by collecting and filtering data from the subsystem autonomous agent, and sends the collected and filtered data to a wayside system.
  • autonomous agent' As used herein, the terms “autonomous agent'”, “subsystem autonomous agent” “train autonomous agent”, “fleet autonomous agent”, and “maintenance autonomous agent”, summed up under the terms “autonomous agents” or “agent”, are each intended to be representative of a software instance run on a computer, which work collaboratively together while connected over a communication network.
  • Embodiments of the invention pertain to a system for the asset management of railway trains, and for methods to operate such a system.
  • embodiments make use of the concept of "active monitoring", which includes an embedded distributed cooperative diagnosis.
  • This diagnosis is then sent to the wayside and is thereby used as information for maintenance action to mitigate failure impact during operation. This is achieved by avoiding failures due to the failure prediction of a predictive diagnosis.
  • maintenance action can be carried out before a failure occurs.
  • failures are understood more thoroughly by providing an accurate diagnosis based on a failure context analysis. The maintenance action is done, for example, to solve the root cause of the failure, and to avoid a second failure, or even potentially costly immobilization of the train due to the need for stationary trouble shooting investigation.
  • the diagnosis system is organized in at least two levels, wherein one level is embedded in each train of a fleet, and the other level is on the ground (on the wayside), whereby a fleet server of the fleet diagnosis system collects outputs of each rolling stock of the fleet.
  • a fleet server of the fleet diagnosis system collects outputs of each rolling stock of the fleet.
  • system on the wayside' is used interchangeably herein to describe a part or parts of the system according to embodiments, which is/are located stationary and apart from the train(s) itself, thus on the wayside.
  • the individual components of the system on the wayside may vary according to specific embodiments and are apparent from the following description.
  • the latter may be performed fully automatic by a central server based on the transmitted data from all rolling stock (all trains in the fleet).
  • the data transmission from rolling stock to ground may be carried out cost effective by an intelligent processing of the raw data and uploading high level, robust and filtered information, including the context idea.
  • the general availability of the fleet of rolling stock is improved, and the latter without any negative impact on safety of commercial operations.
  • An achieved improvement of the reliability of the rolling stock typically leads to a reduction of liquidated damages during warranty phases. Out of warranty, it is a helpful tool for the maintenance service personnel to help for diagnosis, meaning a reduction in time, effort, and thus total cost of ownership.
  • raw data or diagnostic data (diagnostic events) originating from various sources in the train surveillance system are treated as events.
  • connected systems in the train are employed, which are communicatively connected to a system on the wayside.
  • maintenance tools used to record maintenance activity may also be connected to the above system in embodiments, as described further below.
  • the generation of events from surveillance data etc. is dynamic and is carried out according to configuration rules for event manipulation / event processing.
  • threshold values employed in the rules are not fixed and can evolve, while also new rules can be added.
  • This adaption of the rules, or the invention of new rules for the event handling can be carried out by human operators according to their experience.
  • the adaption or generation of rules may at least partly be carried out by machine learning, or differently expressed, by an AI system.
  • the general manner of handling events in the system is based on at least two basic main mechanisms: One is filtering, which means that if an event occurs, and in the same time another event occurs, the second event may erase the first event, if certain conditions are met.
  • An example is the occurrence of a fault, while the second event is that the train is in maintenance, therefore the fault is not propagated as the train is in a maintenance environment anyway.
  • the second mechanism is fusioning (also called aggregation): For example, if all doors in the train would generate the same fault, only one event will be propagated as a synthesis of the situation.
  • the described handling mechanisms for events are carried out by a plurality of cooperating, but independent software instances, henceforth called autonomous agents.
  • the event handling is not carried out by describing or propagating alarm states or signals - rather, events are generated for diagnosis analysis and for monitoring, or for maintenance action.
  • Alarms are generally only employed if absolutely required from the events, that is, if the meaning of the event is analysed as critical, for example a major failure in the HVAC system leading to a strong temperature deviation in the train.
  • the systems and methods according to embodiments disclosed herein are suitable to control the ever growing amount of raw data delivered by surveillance systems with increasing numbers of items which produce raw data.
  • the amount of data having to be transferred to a wayside is reduced and made more valuable and usable by generating contextualized, and thus context dependent, high level, robust knowledge from the raw data.
  • This is achieved by a hierarchical, cooperative monitoring architecture. Thereby, from low level monitoring and surveillance components to the train itself, for each level basically the same monitoring principles and functions apply, whereby with a growing level in the hierarchy, more intelligent data is generated in the form of events, while the overall amount of data is increasingly reduced.
  • the disclosed generic monitoring method is applicable on different complex systems to be monitored, such as railway trains, but also other complex systems, and has the built-in ability to address a higher hierarchic fleet level and the interaction with maintenance centers, which are remote from the monitored system(s), such as typically a maintenance facility on the wayside.
  • the hardware infrastructure does not play a crucial role and may be implemented in various differing manners.
  • the concrete systems described in the following should be regarded as non-limiting, and as examples only.
  • the implementation of the described method may be carried out in different manners, for example implementing various degrees of analysis by software having differing complexity levels.
  • Fig. 1 shows a system for the asset management of railway trains according to embodiments, comprising a diagnosis system 10 which is located in a railway train.
  • the diagnosis system 10 includes a train surveillance system 15 comprising sensors 140, 141, 142, 143 and at least one surveillance subsystem 20, 21 comprising at least one sensor 145. Further, the diagnosis system 10 comprises a train autonomous agent 25 and at least one subsystem autonomous agent 30 realized as computer programs.
  • the train surveillance system 15 and the at least one surveillance subsystem 20, 21 are connected on a network 35 with the train autonomous agent 25 and the at least one subsystem autonomous agent 30 (in Fig. 1 , four subsystem autonomous agents 30, 31, 32, 33 are exemplarily shown, which is non-limiting).
  • the surveillance system and the surveillance subsystem are nodes of the communication network 35, such as are the train autonomous agent 25 and the subsystem autonomous agent 30.
  • the at least one subsystem autonomous agent 30 and the train autonomous agent 25 are adapted to create abstracted information in the form of events, which are time-stamped, as an output. This is carried out by processing input data provided in the form of sensor data and/or diagnosis data.
  • the input data is generally delivered to the agents by the surveillance subsystem(s) 20, 21 and train surveillance system 15 over the communication network 35.
  • the agents process the input data by firstly adding a time stamp to it. Further, the agents are configured to process the input data in a number of manners, which may be carried out by and on each agent independently, or in collaboration between at least two agents. Thereby, input data can be aggregated and/or reduced to achieve information in the form of events.
  • event bus 50 The event bus originates in the train surveillance system 15, as nearly all events (provided some exceptions) in the system are originating from the train surveillance system 15.
  • a local context is defined in the architecture for each device to be monitored.
  • This context comprises known possible fault conditions, and a list of possible operating stati provided by the door operating units, for example.
  • These operating stati may for example include a fault condition of the electric motor of the door operating unit, an overload of the electric motor of the respective door control unit due to a blocking of the door by a foreign object, etc.
  • These operating stati are implemented in at least one of the autonomous agents 30, 31, 32, 33. The sum of the operating stati and fault conditions are employed in a model for various operational states for each monitored device.
  • a train level is defined including a specific context.
  • This context may, for example, comprise speed of the train, driving status, outside temperature, drive motor temperature, status if the train is coupled to another train or not, etc.
  • a train autonomous agent 25 makes use of the previous elements, that is, the context in which the train is operating, in order to process the input from the subsystem autonomous agents.
  • the train autonomous agent provides an embedded cooperative multi-agent-diagnosis summarizing the input from all subsystem autonomous agents.
  • the train autonomous agent 25 may also receive data form other sources than the subsystem autonomous agents 30, 31, 32, 33. It may for example receive events from a surveillance subsystem 20, which is adapted to monitor elements of the train which are not supervised by the subsystem autonomous agents via the train surveillance system 15.
  • the train autonomous agent 25 and the subsystem autonomous agents 30, 31, 32, 33 are adapted to execute rules, which are part of the integrated model, based on a trigger event. This involves, for example, checking other simultaneous or quasi-simultaneous events (in a time window prior and after a trigger event) by checking the presence of the other events within a temporal window around this trigger event. In this manner, the train autonomous agent may, for example, come to the conclusion that a blocking of all doors on one side of the train is due to passengers which still enter the train despite a command for closing the doors was provided.
  • the train autonomous agent When the train autonomous agent receives a first event from a subsystem autonomous agent, that the respective door does not close properly, it will thus, based on an internal rule as part of the model, check if there are other events from other subsystem autonomous agents which indicate the same fault condition.
  • each of the autonomous agents 30, 31, 32, 33 in the train have at least one configuration file.
  • the components of the diagnosis system 10 may be individually updated from the wayside 110. Further and deeper analysis of the status of parts of the train 127 can further be carried out by using log files, which are typically routinely produced during operation, for selected ones or for each of the agents of the diagnosis system 10.
  • the agents have a central and crucial role in the systems and methods described herein.
  • the singular train autonomous agent collects events from the plurality of subsystem autonomous agents.
  • the subsystem autonomous agents are dedicated to watching a specific subsystem, sensor or sensor group each.
  • a subsystem autonomous agent 30 subscribes to a list of events coming from the train surveillance system 15 or another subsystem autonomous agent 31, 32, 33.
  • the subsystem autonomous agent constantly listens, on the communication network 35, for the occurrence of events which he is subscribed to via the list.
  • This list is configurable and may be configured by an operator, for example.
  • the subsystem autonomous agent 30 collects input data in the form of events from the train surveillance system 15.
  • the agent also receives events which are sent to it by another subsystem autonomous agent 31, 32, 33, to which it is connected over the communication network 35, for cooperation with the aim of, for example, fusioning different events or filtering events in a collaborative manner between the agents.
  • the autonomous agents of the system are typically configured to carry out a number of different tasks or processes for processing the input data (events), collaborate with other connected autonomous agents, or to output data for further processing.
  • Agents may temporarily store a time stamped event for further triggering and processing.
  • an agent can trigger a process under the condition of a specified event, wherein the parameters of the trigger are also configurable.
  • autonomous agents are typically adapted to perform at least one of the following processes, wherein the output of each process is typically in the form of a time stamped event.
  • Autonomous agents can "black box" events. Agents may filter raw data and/or diagnosis data coming from a train surveillance system 15 or from another subsystem autonomous agent 31, 32, 33. They may receive and aggregate information from at least two other autonomous agents 31, 32, 33, and aggregate information by collaboration, via the train event bus 50, with at least one other autonomous agent 31, 32, 33.
  • An agent may perform a diagnosis of, for example, a subsystem, when the subsystem autonomous agent is configured to be a diagnostic autonomous agent.
  • Information resulting as output from any of the above described processes carried out by an agent is typically time stamped and sent as an event onto the train event bus 50. From there, it may be received by another subsystem autonomous agent, or by the train autonomous agent 25, or it may be used to create an output on a human interface terminal (user display 77) such as a screen, for example.
  • a subsystem autonomous agent 30, 31, 32, 33 or train autonomous agent 25 is generally configured to aggregate information in the form of events over the event bus 50. This comprises the collection of data from distinct events, wherein two or more events are defined as distinct - and thus treated as distinct by an agent - when one or more of the following criteria are met: a) The two events are timely distinct and originate from the same subsystem or source. b) The two events are stemming from different subsystems, but are collected at (nearly or exactly) the same time by an agent. c) Two events represent or comprise diagnosis data stemming from different subsystem autonomous agents 30, 31, 32, 33. d) The two events belong to a consecutive number of diagnosis events stemming from the same single subsystem diagnostic autonomous agent.
  • the subsystem autonomous agents 30, 31, 32, 33 and train autonomous agent each comprise a rule-based context process.
  • the process includes reducing a number of collected distinct events based on predefined rules.
  • the rules may comprise filtering of single events, as well as collecting several events according to rules, followed by a rule-based deduction that certain of the collected distinct events belong to a single technical cause in a subsystem or system - and thus may be aggregated or some may be filtered.
  • the train autonomous agent 25 and the subsystem autonomous agent 30, 31, 32, 33 may comprise at least one diagnostic process.
  • This diagnostic process can be adapted to check for and determine clear defects, which is also called curative diagnosis.
  • a further diagnostic process can be adapted to check for and determine hidden defects, also called predictive diagnosis.
  • the respective agents having a diagnostic function are called train diagnostic autonomous agent and subsystem diagnostic autonomous agent 30, 31, 32,33.
  • a case of predictive diagnosis might be as follows: If a subsystem delivers data via the train surveillance system as an event, which contains a parameter indicating that the heating power of an HVAC has declined by, e.g. 7% from its nominal value stored in the rules, this may be regarded by the agent as a case of predictive diagnosis. This means, an event is generated as an output for the train autonomous agent 25, which indicates that there is a maintenance need for the HVAC subsystem.
  • the thresholds for this predictive diagnosis are also configurable as lists from the wayside 110.
  • Subsystems 60 which can be subject of such supervision by agents comprise, as non-limiting examples, a single door, a set of doors, an HVAC, electrical and hydraulical systems of the washrooms, a pantograph 68, a bogie 70, or any other subsystem of the rail vehicle.
  • the various agents described herein are typically software instances, such as executable files, which run on a computer.
  • a basic exemplary structure of such an autonomous agent 30, as employed in embodiments, is described in the following with reference to Fig. 2 .
  • the autonomous agent 30 has a configurable list of listened signals 72 with events to which it listens on the communication network 35 to which it is connected. The events are input to the communication network 35 typically by the train surveillance system 15. The agent further listens to events addressed to itself, which have been sent by other autonomous agents on the communication network 35.
  • It has a generic processing unit 73, which processes events on the basis of configurable rules 74 for inhibition (I), fusion (F) and cooperation (C). Further, it comprises a specific processing unit 79 which processes events according to configurable models 78.
  • the output of the autonomous agent is sent to the communication network 35 in the form of events.
  • the events may be addressed to other subsystem autonomous agents on the communication network 35, to the train autonomous agent 25, or to a human interface or user display 77 to be noted by an operator.
  • the autonomous agent further comprises a start/stop processing condition 80, and means 81 for changes memorization. It goes without saying that the skilled person will be able to modify the exemplary structure of an autonomous agent, as described herein, wherein such modifications are regarded to fall under the scope of this disclosure.
  • a method for conducting prognostic and health monitoring assessment for trains employs the above described diagnosis system 10 in a railway train.
  • the subsystem autonomous agents 30, 31, 32, 33 collect and filter data from at least one subsystem diagnostic autonomous agent, and cooperate with at least one other subsystem autonomous agent for data comparison.
  • the train autonomous agent 25 as a central instance in the train, collects and filters data from the train diagnostic autonomous agent, cooperates with a subsystem autonomous agent by collecting and filtering data from the subsystem autonomous agent, and sends the collected and filtered data to a wayside system.
  • This system is a fleet surveillance system 100 as part of a wayside system 110 and comprises an interface for receiving data from each train autonomous agent of each train of a fleet, in real time or near real time.
  • Each train autonomous agent 25 and the subsystem autonomous agents 30, 31, 32, 33 receive data from the respective train surveillance system 15.
  • the fleet surveillance system 100 on the wayside creates a dedicated fleet event bus 120 per train.
  • a fleet autonomous agent 115 executes processes based on, or using, events on the fleet event bus 120.
  • the fleet autonomous agent 115 has basically the same structure and working principle as the subsystem autonomous agents and the train autonomous agent 25 of the train - wherein models, rules and the list of listened signals is configured for the role of the fleet autonomous agent 115, differing from the former.
  • the wayside system 110 further comprises a maintenance surveillance system 105. It transforms human maintenance activities, which are collected in an aided maintenance tool management (database based), into time stamped events.
  • a single maintenance event bus 130 is created.
  • the maintenance surveillance system 105 is connected to the fleet event buses 120 of the trains.
  • the fleet event buses 120 originate in the train autonomous agent 25 of each train and span over the typically wireless connection to the maintenance surveillance system 105 on the wayside.
  • maintenance autonomous agents 125, 126 are connected via the maintenance event bus 130. They realize processes from the event subscription to fleet event buses 120 and the maintenance event bus 130. Decisions on maintenance activities, their planning and optimization are derived by combining maintenance events from the train autonomous agents 25 via the fleet surveillance system and with data from human maintenance activities via maintenance surveillance system 105 and maintenance autonomous agents 125, 126. Thereby, fleet availability and maintenance quality are enhanced while rationalizing and reducing overall maintenance activities and thus also cost.
  • log files are provided by the respective autonomous agents, which allow an operator to understand with accuracy the nature and cause(s) of complex failures. Simultaneously, the maintainer may define corrective action to avoid that a failure might cause further, or more significant, damage.
  • a method 200 for conducting prognostic and health monitoring is schematically shown, wherein only the basic method steps are depicted.
  • data is collected and filtered on a subsystem level in the train.
  • the autonomous agents cooperate with each other, as was described further above.
  • data in the form of events is collected and filtered on a train level by the train autonomous agent.
  • the resulting data is sent in the form of events to a wayside system 110.
  • the data is processed on the fleet event bus.
  • maintenance activities are scheduled.

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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)
EP16174036.0A 2016-06-10 2016-06-10 System und verfahren zur anlagenverwaltung von eisenbahnzügen Pending EP3254928A1 (de)

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EP16174036.0A EP3254928A1 (de) 2016-06-10 2016-06-10 System und verfahren zur anlagenverwaltung von eisenbahnzügen
PCT/EP2017/062623 WO2017211593A1 (en) 2016-06-10 2017-05-24 System and method for the asset management of railway trains

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CN111661113A (zh) * 2020-05-26 2020-09-15 神华新朔铁路有限责任公司 一种轨道交通车辆的运维方法及系统
EP3753801A1 (de) * 2019-06-17 2020-12-23 Mitsubishi Heavy Industries, Ltd. Überwachungssystem für eine infrastruktur und/oder ein fahrzeug mit ereigniserkennung
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WO2023200596A1 (en) * 2022-04-14 2023-10-19 Bnsf Railway Company Automated positive train control event data extraction and analysis engine and method therefor
WO2023200597A1 (en) * 2022-04-14 2023-10-19 Bnsf Railway Company Automated positive train control event data extraction and analysis engine for performing root cause analysis of unstructured data
US11861509B2 (en) 2022-04-14 2024-01-02 Bnsf Railway Company Automated positive train control event data extraction and analysis engine for performing root cause analysis of unstructured data
US11897527B2 (en) 2022-04-14 2024-02-13 Bnsf Railway Company Automated positive train control event data extraction and analysis engine and method therefor

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