Engineering knowledge-based condition analyzers for on-board intelligent fault classification: a case study
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- Author(s): C. Brignone 1 ; C. De Ambrosi 1 ; M. De Luca 1 ; F. Narteni 1 ; A. Tacchella 1 ; S. Verstichel 1 ; G. Villa 1
- Conference: 4th IET International Conference on Railway Condition Monitoring (RCM 2008)
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Source:
4th IET International Conference on Railway Condition Monitoring (RCM 2008),
January 2008
page
19
Affiliations:
1:
Bombardier Transp. Italy S.p.A., Vado Ligure
, Italy
- DOI: 10.1049/ic:20080326
- ISBN: 978 0 86341 927 0
- Location: Derby, UK
- Conference date: 18-20 June 2008
- Format: PDF
In this paper we describe the design of a knowledge-based condition analyzer that performs on-board intelligent fault classification. The system is designed to be deployed as a prototype on E414 locomotives, a series of downgraded highspeed vehicles that are currently employed in standard passenger service. Our goal is to satisfy the requirements of a development scenario in the Integrail project for a condition analyzer that leverages an ontology-based description of some critical E414 subsystems in order to classify faults considering mission and safety related aspects. (6 pages)
Inspec keywords: ontologies (artificial intelligence); condition monitoring; locomotives; fault diagnosis; railway engineering; engineering computing
Subjects: Railway industry; Products and commodities; Maintenance and reliability; Computing in other engineering fields; Knowledge engineering techniques; Inspection and quality control

