Fault analysis of condenser based on RBF network and d-s evidence theory

  • Authors:

  • Fei Xia;Hao Zhang;Wei Liu;Daogang Peng;Hui Li;Cunmei Xu

  • Affiliations:

  • College of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China,CIMS Research Center, Tongji University, Shanghai, China;College of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China,CIMS Research Center, Tongji University, Shanghai, China;Shanghai Chinaust Plastics Corp., Ltd., Shanghai, China;College of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China;College of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China;College of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China

  • Venue:
  • AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
  • Year:
  • 2012

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Abstract

A novel Information fusion fault diagnosis method is proposed for condenser fault analysis. Condenser fault diagnoses were analyzed by two algorithms of Radical Basis Function (RBF) neural network. And then the method of information fusion diagnosis was used for improving the results form the two networks. This method has both advantages of the simple features of neural networks and the uncertainty capabilities of information fusion in the application. Through the condenser fault simulation test, it can be verified to improve the accuracy of fault diagnosis, while reducing the complexity of the algorithm.