Performance degradation analysis and fault prognostics of solid oxide fuel cells using the data-driven method

Widespread commercial implementation of the solid oxide fuel cell (SOFC) systems is hindered by their high cost, insufficient durability and poor reliability. Fault prognostics of these systems are extremely difficult due to the complicated interactions of their constituting components. This paper proposes a data-driven method of fault prognostics of SOFC systems based on the voltage signal. The voltage signal is first decomposed into a trend component and several fluctuation components with the empirical mode decomposition (EMD). The minimal-redundancy-maximal-relevance criterion (mRMR) is then applied to determine the most relevant fluctuation component. A Gauss mixture model with different humps is obtained from the distribution of the trend component and the fluctuation component in different periods. Finally, the similarity of different humps is calculated and adopted as the health indicator (HI). A fault warning is successfully issued approximately 70 h in advance. Meanwhile, the validity of the proposed method is confirmed by the measured microstructure and element distribution at different degradation stages using the scanning electron microscopy (SEM) and energy dispersive X-ray detector (EDX). These results demonstrate that the proposed method can predict the fault occurrence during the SOFC operation.