Title : 
Engineering knowledge-based condition analyzers for on-board intelligent fault classification: A case study
         
        
            Author : 
Brignone, C. ; De Ambrosi, C. ; de Luca, M. ; Narteni, F. ; Tacchella, Armando ; Verstichel, Stijn ; Villa, G.
         
        
            Author_Institution : 
Bombardier Transp. Italy S.p.A., Vado Ligure
         
        
        
        
        
        
            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 : 
condition monitoring; engineering computing; fault diagnosis; locomotives; ontologies (artificial intelligence); railway engineering; E414 locomotives; Integrail project; condition analyzers; engineering knowledge; on-board intelligent fault classification; ontology; railway transportation; Artificial Intelligence; Fault Classification; Reasoning about Knowledge; Software Engineering;
         
        
        
        
            Conference_Titel : 
Railway Condition Monitoring, 2008 4th IET International Conference on
         
        
            Conference_Location : 
Derby
         
        
        
            Print_ISBN : 
978-0-86341-927-0