comprehensive analysis of fault diagnosis methods for aluminum electrolytic control system

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ID: 217353
2014
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Abstract
This paper established the fault diagnosis system of aluminum electrolysis, according to the characteristics of the faults in aluminum electrolysis. This system includes two subsystems; one is process fault subsystem and the other is fault subsystem. Process fault subsystem includes the subneural network layer and decision fusion layer. Decision fusion neural network verifies the diagnosis result of the subneural network by the information transferring over the network and gives the decision of fault synthetically. EMD algorithm is used for data preprocessing of current signal in stator of the fault subsystem. Wavelet decomposition is used to extract feature on current signal in the stator; then, the system inputs the feature to the rough neural network for fault diagnosis and fault classification. The rough neural network gives the results of fault diagnosis. The simulation results verify the feasibility of the method.
Reference Key
li2014advancescomprehensive Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Jie-jia Li;Xiao-yan Han;Peng Zhou;Xiao-yu Sun;Na Chang
Journal bulletin of the korean chemical society
Year 2014
DOI
10.1155/2014/975317
URL
http://dx.doi.org/10.1155/2014/975317
Keywords
materials of engineering and construction mechanics of materials

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