Multi-model and multi-level aluminum electrolytic fault diagnosis method

Abstract

A multi-model and multi-level aluminum electrolytic fault prediction method is proposed. In this method, it innovatively uses the image recognition technology to predict aluminum electrolytic faults, and superimposes the chaotic neural network model to form a dual-model parallel fault prediction system for aluminum electrolysis, which can obtain more faults information from different angles. Then, it designs the decision fusion layer, which combines the prediction results of the above two models to output the final prediction results and enhances the credibility of the prediction results. In addition, the data processing stage also uses principal component analysis (PCA) to extract the main features of fault information, which reduces the data dimension and speeds up the processing. Experimental results suggest that the proposed algorithm can predict faults in an effective manner, and outperform other algorithms in terms of accuracy, sensitivity and stability.