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Vol. 177, Issue 8, August 2014, pp. 122-127




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

Jiejia Li, Xiaoyan Han, Yongqiang Chen

School of Information and Control Engineering, Shenyang Jianzhu University,
Shenyang, 110168, China


Received: 20 May 2014 /Accepted: 31 July 2014 /Published: 31 August 2014

Digital Sensors and Sensor Sysstems


Abstract: 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.


Keywords: Aluminum electrolysis, Fault diagnosis, ICA algorithm, Rough neural network.


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