A microbiological fuel cell fault diagnosis method based on wavelet-transformation is disclosed and relates to fuel cell fault diagnosis method. According to the method, output voltage signals acquired under different fault statuses are analyzed by utilizing frequency multiplication wavelet, reconstruction of high- and low-band signals is realized by calculating the wavelet coefficient , the fault-signal frequency-domain characteristic constant is extracted, and the main distribution status of the fault signals is displayed, and thus battery faults are diagnosed. The method is capable of realizing fault diagnosis of a microbiological fuel cell system, gives play to effects of early alarming, and reducing and avoiding fault scope, guarantees safe reliable operation of equipment, and possesses relatively high application value during actual engineering.