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Engineering knowledge-based condition analyzers for on-board intelligent fault classification: a case study

C Brignone, C De Ambrosi, M De Luca, F Narteni, A Tacchella, Stijn Verstichel (UGent) and G Villa
(2008) 4th IET International Conference on Railway Condition Monitoring - RCM 2008.
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Abstract
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.
Keywords
E414 locomotives, railway transportation, ontology, engineering knowledge, condition analyzers, on-board intelligent fault classification, Integrail project, condition monitoring, engineering computing, fault diagnosis, locomotives, ontologies (artificial intelligence), railway engineering

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Citation

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MLA
Brignone, C., et al. “Engineering Knowledge-Based Condition Analyzers for on-Board Intelligent Fault Classification: A Case Study.” 4th IET International Conference on Railway Condition Monitoring - RCM 2008, IET, 2008.
APA
Brignone, C., De Ambrosi, C., De Luca, M., Narteni, F., Tacchella, A., Verstichel, S., & Villa, G. (2008). Engineering knowledge-based condition analyzers for on-board intelligent fault classification: a case study. 4th IET International Conference on Railway Condition Monitoring - RCM 2008. Presented at the 4th IET International conference on Railway Condition Monitoring (RCM 2008), Derby, UK.
Chicago author-date
Brignone, C, C De Ambrosi, M De Luca, F Narteni, A Tacchella, Stijn Verstichel, and G Villa. 2008. “Engineering Knowledge-Based Condition Analyzers for on-Board Intelligent Fault Classification: A Case Study.” In 4th IET International Conference on Railway Condition Monitoring - RCM 2008. Stevenage, UK: IET.
Chicago author-date (all authors)
Brignone, C, C De Ambrosi, M De Luca, F Narteni, A Tacchella, Stijn Verstichel, and G Villa. 2008. “Engineering Knowledge-Based Condition Analyzers for on-Board Intelligent Fault Classification: A Case Study.” In 4th IET International Conference on Railway Condition Monitoring - RCM 2008. Stevenage, UK: IET.
Vancouver
1.
Brignone C, De Ambrosi C, De Luca M, Narteni F, Tacchella A, Verstichel S, et al. Engineering knowledge-based condition analyzers for on-board intelligent fault classification: a case study. In: 4th IET International Conference on Railway Condition Monitoring - RCM 2008. Stevenage, UK: IET; 2008.
IEEE
[1]
C. Brignone et al., “Engineering knowledge-based condition analyzers for on-board intelligent fault classification: a case study,” in 4th IET International Conference on Railway Condition Monitoring - RCM 2008, Derby, UK, 2008.
@inproceedings{885302,
  abstract     = {{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.}},
  author       = {{Brignone, C and De Ambrosi, C and De Luca, M and Narteni, F and Tacchella, A and Verstichel, Stijn and Villa, G}},
  booktitle    = {{4th IET International Conference on Railway Condition Monitoring - RCM 2008}},
  keywords     = {{E414 locomotives,railway transportation,ontology,engineering knowledge,condition analyzers,on-board intelligent fault classification,Integrail project,condition monitoring,engineering computing,fault diagnosis,locomotives,ontologies (artificial intelligence),railway engineering}},
  language     = {{eng}},
  location     = {{Derby, UK}},
  pages        = {{6}},
  publisher    = {{IET}},
  title        = {{Engineering knowledge-based condition analyzers for on-board intelligent fault classification: a case study}},
  year         = {{2008}},
}