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Expert system based fault diagnosis for railway point machines

Reetz, Susanne and Neumann, Thorsten and Schrijver, Gerrit and van den Berg, Arnout and Buursma, Douwe (2023) Expert system based fault diagnosis for railway point machines. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. SAGE Publications. doi: 10.1177/09544097231195656. ISSN 0954-4097.

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Official URL: https://journals.sagepub.com/doi/full/10.1177/09544097231195656


To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e. the "fault symptoms"). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g. past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets.

Item URL in elib:https://elib.dlr.de/189682/
Document Type:Article
Title:Expert system based fault diagnosis for railway point machines
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Reetz, SusanneUNSPECIFIEDhttps://orcid.org/0000-0002-5096-6327UNSPECIFIED
Neumann, ThorstenUNSPECIFIEDhttps://orcid.org/0000-0002-9236-0585UNSPECIFIED
Schrijver, GerritStrukton RailUNSPECIFIEDUNSPECIFIED
van den Berg, ArnoutStrukton RailUNSPECIFIEDUNSPECIFIED
Date:6 November 2023
Journal or Publication Title:Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:SAGE Publications
Keywords:railway switch, fault diagnosis, Bayesian networks, expert knowledge, prognostics and health management
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Rail Transport
DLR - Research area:Transport
DLR - Program:V SC Schienenverkehr
DLR - Research theme (Project):V - TraCo - Train Control and Management
Location: Berlin-Adlershof , Braunschweig
Institutes and Institutions:Institute of Transportation Systems > Information Gathering and Modelling, BA
Institute of Transportation Systems > Information Gathering and Modelling, BS
Deposited By: Reetz, Susanne
Deposited On:10 Nov 2023 13:47
Last Modified:10 Nov 2023 13:47

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