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In this paper, a model-based fault diagnosis
methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals using a LPV observer. Fault detection is based on using adaptive threshold generated using an interval observer. Fault isolation is performed
using the Euclidean distance between observed relative residuals and theoretical relative sensitivities. To illustrate
the results, the commercial fuel cell Ballard Nexa c is used in simulation where a set of typical fault scenarios have
been considered. Finally, the diagnosis results corresponding to those fault scenarios are presented. It is remarkable that with this methodology it is possible to diagnose and isolate all the considered faults in contrast with other well known
methodologies which use the classic binary signature matrix approach.
CitacióPuig, V.; Quevedo, J.; de Lira, S. LPV Model-Based Fault Diagnosis Using Relative Fault Sensitivity Signature Approach in PEM Fuel Cell. A: IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. "7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes". Barcelona: ESAII-CT, 2009, p. 528-533.