Fault Diagnosis Scheme Based on Microbial Fuel Cell Model

Around the world, fossil fuels are decreasing and pollution is increasing. As a new energy source, microbial fuel cells (MFCs) have been widely concerned. However, most of the previous researches focused on the material selection, configuration design and optimal control of MFCs, and few of them were able to systematically analyze the failures of MFCs. In order to ensure the reliable operation of MFCs, this paper systematically explores the MFC fault diagnosis process, including the acquisition of initial fault data, feature extraction and fault classification.Firstly, in order to acquire data quickly and effectively, the mathematical model is used to simulate the occurrence of faults, and four types of typical fault voltages are obtained. Then, wavelet analysis is used to extract the voltage characteristics of MFC faults, and the characteristics of each fault are explored in eight frequency bands. Finally, the recognition effects of various classifiers on fault features are compared. Through the analysis of the results, it is found that fault tree is the most suitable fault diagnosis method for MFCs. The fault data extraction method proposed in this paper and the classification effect of various classifiers finally obtained provide a reference for the further analysis of MFC faults. At the same time, the combination of wavelet analysis and fault tree diagnosis model proposed in this paper provides ideas for fault diagnosis in other fields.