Multiple Fault Diagnosis Research on Motors in Aluminum Electrolytic Based on ICA Feature Extraction

Motors as the actuator in the aluminum electrolysis process, mainly used for control the lifting of the anode to control the cell voltage, make the electrolytic tank keep in the best condition, once the motors failed, slot voltage will be out of control. This paper study on the fault of the motors in the process of aluminum electrolysis. In this paper adopts the EMD algorithm for various stator current signal data preprocessing, it has high adaptive decomposition ability and good ability, and then use the ICA algorithm extract feature of the current which has been denied. The extracted features input to the rough neural network for fault diagnosis and classification and gives the results of fault diagnosis. Through the simulation and analysis verify the feasibility and superiority of this model. Copyright © 2014 IFSA Publishing, S. L.