Abstract
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.
Highlights
D UE to the global reduction of traditional energy and the environmental damage caused by the use and development of fossil energy, renewable new energy has attracted worldwide attention
microbial fuel cells (MFCs) system itself has strong coupling, so it is difficult to trace the fault cause, which seriously affects the efficiency of electricity generation
The fault voltage of MFC was extracted by parameter perturbation in MFC model, and four typical original fault characteristics of MFC were obtained
Summary
D UE to the global reduction of traditional energy and the environmental damage caused by the use and development of fossil energy, renewable new energy has attracted worldwide attention. MFC is special because it uses microorganisms as catalyst, which makes it difficult to diagnose its faults.Deep learning is widely used in data classification and has a good effect. MFC driven by microorganisms is gradually manifested over time after most of the faults occur This determines that it is difficult for MFC to conduct deep learning by collecting a large amount of fault information accurately.Yan et al proposed an MFC fault feature recognition method based on wavelet analysis [13]. Based on the above research, wavelet analysis can effectively describe local features in the time domain and frequency domain It has a good application in fault diagnosis, especially for nonlinear systems. 3) A hybrid learning model based on wavelet analysis and fault tree is proposed, and its fault diagnosis effect in MFC is verified.
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