Comprehensive Analysis of Fault Diagnosis Methods for Aluminum Electrolytic Control System
Joint Authors
Li, Jie-jia
Han, Xiao-yan
Zhou, Peng
Sun, Xiao-yu
Chang, Na
Source
Advances in Materials Science and Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-21
Country of Publication
Egypt
No. of Pages
7
Abstract EN
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.
American Psychological Association (APA)
Li, Jie-jia& Han, Xiao-yan& Zhou, Peng& Sun, Xiao-yu& Chang, Na. 2014. Comprehensive Analysis of Fault Diagnosis Methods for Aluminum Electrolytic Control System. Advances in Materials Science and Engineering،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1015930
Modern Language Association (MLA)
Li, Jie-jia…[et al.]. Comprehensive Analysis of Fault Diagnosis Methods for Aluminum Electrolytic Control System. Advances in Materials Science and Engineering No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1015930
American Medical Association (AMA)
Li, Jie-jia& Han, Xiao-yan& Zhou, Peng& Sun, Xiao-yu& Chang, Na. Comprehensive Analysis of Fault Diagnosis Methods for Aluminum Electrolytic Control System. Advances in Materials Science and Engineering. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1015930
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1015930