Research on fault diagnosis method of aluminum electrolytic cell based on feature extraction

Aluminum electrolytic cell voltage signal is the most important real-time data in the process of aluminum electrolysis. Under normal circumstances, the traditional LMD decomposition of the original voltage signal containing noise in the aluminum electrolytic cell will produce a large error and affect the diagnosis of the failure of the aluminum electrolytic cell. In this paper, an improved LMD decomposition method is used to decompose the cell voltage signal based on wavelet packet denoising. At the same time, according to the energy analysis, the extracted features are classified. The accuracy and reliability of feature extraction can be improved, and the faults existing in the aluminum electrolytic cell can be accurately diagnosed.