Volume 32 Issue 2
Jan.  2018
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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2003  >  32(2): 212-216

Liu Jianchen, Jiang Xinhua, Wu Jinpei. Research of Intelligent Various Structure Models in Motor Faults Diagnosis[J]. Journal of University of Electronic Science and Technology of China, 2003, 32(2): 212-216.
Citation: Liu Jianchen, Jiang Xinhua, Wu Jinpei. Research of Intelligent Various Structure Models in Motor Faults Diagnosis[J]. Journal of University of Electronic Science and Technology of China, 2003, 32(2): 212-216.
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Research of Intelligent Various Structure Models in Motor Faults Diagnosis

  • 1.

    School of Information Engineering,Central South University ChangSha 410075

  • Received Date: 2002-08-28
  • Publish Date: 2003-04-15
  • The paper puts forward a set of faults diagnosis methods of testing the noise of the three-phase motor current which shows the characteristics of the motor faults. and multi-varieties time series models of the noise is established, the multi-varieties residual series is changed to the monistical as the fault detection index. In the fight of trait of the model parameter φi, presenting a faults classification recognition model based on the multi -layer NN structure, using APEX network extracts classification Information of the initial pattern vector,making use of feedforward network establishes the classification function. The diagnosis way is correct by practising.
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  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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Research of Intelligent Various Structure Models in Motor Faults Diagnosis

  • 1. School of Information Engineering,Central South University ChangSha 410075
  • Received Date: 2002-08-28
  • Publish Date: 2003-04-15

Abstract: The paper puts forward a set of faults diagnosis methods of testing the noise of the three-phase motor current which shows the characteristics of the motor faults. and multi-varieties time series models of the noise is established, the multi-varieties residual series is changed to the monistical as the fault detection index. In the fight of trait of the model parameter φi, presenting a faults classification recognition model based on the multi -layer NN structure, using APEX network extracts classification Information of the initial pattern vector,making use of feedforward network establishes the classification function. The diagnosis way is correct by practising.

Liu Jianchen, Jiang Xinhua, Wu Jinpei. Research of Intelligent Various Structure Models in Motor Faults Diagnosis[J]. Journal of University of Electronic Science and Technology of China, 2003, 32(2): 212-216.
Citation: Liu Jianchen, Jiang Xinhua, Wu Jinpei. Research of Intelligent Various Structure Models in Motor Faults Diagnosis[J]. Journal of University of Electronic Science and Technology of China, 2003, 32(2): 212-216.
shu

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