Fault diagnosis in fuel cell systems using quantitative models and support vector machines. Issue 11 (1st May 2014)

• Record Type: Journal Article
• Authors: Pellaco, L.; Costamagna, P.; De Giorgi, A.; Greco, A.; Magistri, L.; Moser, G.; Trucco, A.
• Is Part Of: Electronics letters. Volume 50:Issue 11(2014)
• Page Start: 824
• Page End: 826
• Publication Date: 2014-05-01
• Subjects: support vector machines -- solid oxide fuel cells -- fault diagnosis -- fuel cell power plants -- power engineering computing; fault diagnosis -- fuel cell systems -- quantitative model -- support vector machines -- fault detection -- fault identification -- electrical generation plants -- solid oxide fuel cells -- supervised classification system -- easy‐to‐measure features -- classification error bound maximisation -- faulty condition -- fault class; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2014.0565
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Fault detection and identification are new and challenging tasks for electrical generation plants that are based on solid oxide fuel cells. The use of a quantitative model of the plant together with a support vector machine to design and operate a supervised classification system is proposed. This type of system, which uses a few easy‐to‐measure features selected through the maximisation of a classification error bound, proved to be effective in revealing a faulty condition and identifying it among the four considered fault classes.