Solid oxide fuel cell fault diagnosis and ageing estimation based on wavelet transform approach

• A novel signal-based approach is developed for the PEMFC fault diagnosis based on the time-frequency analysis.
• Wavelet transform combined with the energy study is proposed for fast fault diagnosis.
• The relative wavelet energy allows the energy contents behaviours depending on different operating conditions.
• A classification approach is developed to validate the state-of-health estimation.
• Results with experimental data verify the effectiveness of the method.

The paper aims at developing a signal-based diagnosis tool diagnosing a high temperature fuel cell named solid oxide fuel cell (SOFC). The wavelet transform (WT) has been used to decompose the SOFCs voltage signals and to find out the effective feature variables that are discriminative for distinguishing the normal and abnormal operating conditions of the system. The diagnosis method is used to detect and isolate SOFC system fault by using the fuel cell stack as a sensor. Considering this, on-line fault detection without any additional sensor is available.