Application of the state deterioration evolution based on bi-spectrum entropy and HMM in wind turbine

Concerning the problem of large rotating machinery with non-stationary state like wind turbine, this research mainly makes an emphasis on the method of state deterioration recognition based on bi-spectrum entropy and HMM (Hidden Markov Model). Firstly, the true signal such as low-speed start vibrati...

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Bibliographic Details
Published inChaos, solitons and fractals Vol. 89; pp. 160 - 168
Main Authors Liu, Xiuli, Xu, Xiaoli, Jiang, Zhanglei, Wu, Guoxin, Zuo, Yunbo
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2016
Subjects
Bi-spectrum entropy
HMM
Pattern recognition
State deterioration
Wind turbine
HMM
Bi-spectrum entropy
Pattern recognition
Wind turbine
State deterioration
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Summary:Concerning the problem of large rotating machinery with non-stationary state like wind turbine, this research mainly makes an emphasis on the method of state deterioration recognition based on bi-spectrum entropy and HMM (Hidden Markov Model). Firstly, the true signal such as low-speed start vibration signals of rotor test rig in the normal state and a plurality of imbalance deterioration degrees are collected. Bi-spectrum is applied to obtain the fault feature from the vibration signals mixed with a complex background noise. On the basis of bi-spectrum analysis, a bi-spectrum entropy algorithm is derived under the condition of subspace distribution probability, and the HMM for the fault pattern recognition is established by using the bi-spectrum entropy feature as input. This method is verified by successfully recognizing four state deterioration degrees. Finally, the method is applied to recognize the imbalance deterioration degree of wind turbine with the type of SL1500/82 and equipment actual working condition verified the effectiveness of the proposed method.
ISSN:0960-0779
1873-2887
DOI:10.1016/j.chaos.2015.10.018