Online implementation of SVM based fault diagnosis strategy for PEMFC system
Résumé
This paper deals with the online diagnosis of Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems. The pattern classification tool Support Vector Machine (SVM) is used to achieve fault detection and isolation (FDI). The algorithm is integrated into an embedded system of the type System in Package (SiP) and validated online in an experimental platform. Four concerned faults are diagnosed successfully online. Additionally, a procedure is proposed to improve the performance of robustness and raise the diagnosis accuracy.
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