Kernel independent component analysis and dynamic selective neural network ensemble for fault diagnosis of steam turbine

  • Authors:

  • Dongfeng Wang;Baohai Huang;Yan Li;Pu Han

  • Affiliations:

  • Department of Automation, North China Electric Power University, Baoding Hebei, China;Department of Automation, North China Electric Power University, Baoding Hebei, China;Department of Automation, North China Electric Power University, Baoding Hebei, China;Department of Automation, North China Electric Power University, Baoding Hebei, China

  • Venue:
  • ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
  • Year:
  • 2010

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Abstract

A new method for fault diagnosis of steam turbine based on kernel independent component analysis (KICA) and dynamic selective neural network ensemble is proposed Firstly, the fault data of steam turbine is analyzed using KICA to extract main features from high dimensional patterns Not only is the diagnosing efficiency improved but also the diagnosing accuracy is ensured Then, the generalization errors of different neural networks to each validating sample are calculated and the information is collected into a performance matrix, according to which the K-nearest neighbor algorithm is used to predict the generalization errors of different neural networks to each testing sample Lastly, the individual networks whose generalization errors are in a threshold λ will be dynamically selected and the predictions of the component neural networks are combined through majority voting The practical applications in fault diagnosis of steam turbine show that the proposed approach gives promising results on performance even with smaller learning samples, and it has higher accuracy and stability.