Online implementation of SVM based fault diagnosis strategy for PEMFC systems. (15th February 2016) - Li, Zhongliang; Outbib, Rachid; Giurgea, Stefan; Hissel, Daniel; Jemei, Samir; Giraud, Alain; Rosini, Sebastien
Highlights: A classification based fault diagnosis approach is proposed for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems. Individual fuel cell voltages are considered as the variables for diagnosis. A high-compacted embedded system is designed and fabricated to realize the approach. Online implementation is carried out to validate the strategy. A diagnosis rule is proposed to improve the robustness and accuracy. Abstract: In this paper, the topic of online diagnosis for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems is addressed. In the diagnosis approach, individual cell voltages are used as the variables for diagnosis. The pattern classification tool Support Vector Machine (SVM) combined with designed diagnosis rule is used to achieve fault detection and isolation (FDI). A highly-compacted embedded system of the System in Package (SiP) type is designed and fabricated to monitor individual cell voltages and to perform the diagnosis algorithms. For validation, the diagnosis approach is implemented online on PEMFC experimental platform. Four concerned faults can be detected and isolated in real-time.