Fault diagnosis in fuel cell systems using quantitative models and support vector machines. Issue 11 (1st May 2014) - Pellaco, L.; Costamagna, P.; De Giorgi, A.; Greco, A.; Magistri, L.; Moser, G.; Trucco, A.
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.