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《Light Metals》 2019-10
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Aluminum electrolysis abnormal state diagnosis method based on cuckoo support vector machine and deep learning

Li Jiejia;Gao Tianhao;Ji Xinyang;School of Information and Control Engineering, Shenyang Jianzhu University;School of Information and Control Engineering, Shenyang Urban Construction University;  
Aiming at problems of traditional aluminum electrolysis abnormal state diagnosis algorithm, such as complex structure, high CPU usage and low accuracy, a cuckoo support vector machine and deep learning cascade abnormal state diagnosis algorithm are designed. Firstly, based on the fast and accurate characteristics of the support vector machine algorithm to deal with the two-category problem, the production status is diagnosed as normal or abnormal, and it is optimized by the new generation cuckoo algorithm to accelerate the convergence speed. If the diagnosis result is normal, the diagnosis result is directly output. If abnormal, the data shall be sent to the next level. The next level is played by an improved deep confidence neural network with good classification effect. This level accepts the information of the upper level, and comprehensively analyzes the type of aluminum electrolysis abnormal state, and diagnoses the specific abnormal state type. Finally, Matlab is used to carry out experimental simulation, the results show that the average usage rate of the algorithm for computer CPU is much lower than the traditional algorithm, while the training speed, accuracy rate and forecast advance value are all obviously improved.
【CateGory Index】: TF821
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