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Bayesian network design for fault diagnostics of railway switches

Neumann, Thorsten and Narezo Guzman, Daniela (2019) Bayesian network design for fault diagnostics of railway switches. In: Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019, pp. 1117-1124. Research Publishing Services. 29th European Safety and Reliability Conference (ESREL 2019), 22.-26. Sept. 2019, Hannover, Deutschland. doi: 10.3850/978-981-11-2724-3_0103-cd. ISBN 978-981112724-3.

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Official URL: http://itekcmsonline.com/rps2prod/esrel2019/e-proceedings/index.html

Abstract

Besides detecting failures and predicting future health conditions of technical systems, fault diagnosis (i.e., fault identification) is a key challenge in the analytic part of prognostics and health management (PHM). In this context, Bayesian networks (BN) has proven to be an effective tool for diagnostic reasoning about faults and effects. Since it is possible to generate such models not only from data but also from expert knowledge or a combination of both (hybrid approach), Bayesian networks are well-suited for many applications and (technical) disciplines. This, in particular, holds for situations where common data-driven approaches (e.g., neural networks, deep learning) suffer from a lack of a reasonable amount of adequate training data. This contribution discusses the detailed design of a comprehensive Bayesian network for railway switches as to be used for fault diagnosis in context of corrective and/or predictive maintenance, for instance. The new model explicitly pursues the modular paradigm of object-oriented Bayesian networks (OOBN), and thus provides a maximum degree of flexibility when adapting it to different types of railway switches. Moreover, it contains Bayesian nodes that act as a kind of "ON/OFF switches" and allow to (de-)activate specific parts of the model without affecting its overall structure. This, in particular, is useful whenever the general Bayesian network comprises modules (e.g., point heater or back drive) that are not available to all switches in the field. Finally, the model benefits from a newly developed, innovative design principle for Bayesian networks which, based on a generalization of the idea of Boolean clusters, reduces (or potentially even completely avoids) the problematic effect of overconfidence in diagnostic reasoning.

Item URL in elib:https://elib.dlr.de/122317/
Document Type:Conference or Workshop Item (Speech)
Title:Bayesian network design for fault diagnostics of railway switches
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Neumann, ThorstenThorsten.Neumann (at) dlr.dehttps://orcid.org/0000-0002-9236-0585
Narezo Guzman, DanielaDaniela.NarezoGuzman (at) dlr.dehttps://orcid.org/0000-0001-9748-1354
Date:24 September 2019
Journal or Publication Title:Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI :10.3850/978-981-11-2724-3_0103-cd
Page Range:pp. 1117-1124
Editors:
EditorsEmailEditor's ORCID iD
Beer, MichaelUNSPECIFIEDUNSPECIFIED
Zio, EnricoUNSPECIFIEDUNSPECIFIED
Publisher:Research Publishing Services
ISBN:978-981112724-3
Status:Published
Keywords:Prognostics and health management, fault diagnosis, railway switches, Bayesian network, object-oriented, overconfidence, hybrid modeling
Event Title:29th European Safety and Reliability Conference (ESREL 2019)
Event Location:Hannover, Deutschland
Event Type:international Conference
Event Dates:22.-26. Sept. 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Next Generation Railway Systems III (old), V - Digitalisierung und Automatisierung des Bahnsystems
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems > Data Management and Knowledge Discovery
Deposited By: Neumann, Dr.-Ing. Thorsten
Deposited On:20 Nov 2019 09:42
Last Modified:20 Jun 2021 15:51

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