A two-stage classifier switchable aluminum electrolysis fault diagnosis method

Abstract

Traditional aluminum electrolysis fault diagnosis methods have problems such as low accuracy, small forecast advance, and high CPU usage, which make their popularity low in enterprises. Aiming at the above problems, a fault diagnosis method with switchable two-level classifiers is designed. The input data are first judged by the first-level algorithm. If it is determined that there is no fault, the result will be output directly. If it is determined that there is a fault in the electrolytic cell, the data will be transferred to the second-level network for specific fault diagnosis. The first level is based on the Random Forest algorithm with simple structure and good two-class classification effect and is optimized by the improved cuckoo algorithm. The second level is based on an improved DBN-DNN (Deep Belief Neural Network–Deep Neural Network) algorithm, and the training method is given. Experimental results show that this method can switch between different algorithms according to different situations, save computing resources, realize that a computer can monitor multiple electrolytic cells, and reduce investment costs. In addition, the accuracy and forecast advance have been significantly improved, which has promoted the popularization of fault diagnosis systems in aluminum electrolysis enterprises.