Application of Adaptive Local Iterative Filtering and Fuzzy Entropy in Gear Fault Identification

Aiming at the problem that the measured signal of gear system can’t accurately reflect the fault characteristics due to noise interference， a fault identification method combining adaptive local iterative filtering and fuzzy entropy is proposed. By using adaptive local iterative filtering， the nonst...

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Bibliographic Details
Published inJixie Chuandong Vol. 45; pp. 146 - 152
Main Authors Wenbin Zhang, Jie Jiang, Yasong Pu, Libin Yu, Dewei Guo, Jie Min
Format Journal Article
LanguageChinese
Published Editorial Office of Journal of Mechanical Transmission 01.05.2021
Subjects
Fault analysis;Gear;Signal processing;Adaptive local iterative filtering;Fuzzy entropy
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Summary:Aiming at the problem that the measured signal of gear system can’t accurately reflect the fault characteristics due to noise interference， a fault identification method combining adaptive local iterative filtering and fuzzy entropy is proposed. By using adaptive local iterative filtering， the nonstationary signals of gears can be decomposed into finite stationary intrinsic mode functions. Since the adaptive local iterative filtering can effectively separate the rotating frequency signals of gear system， the fuzzy entropy of the first several intrinsic mode functions is calculated based on the intrinsic mode functions corresponding to the rotating frequency signals. Finally， grey relevance degree of the vibration signal’s fuzzy entropy under different working conditions is calculated to identify different fault types of gear system. The results show that the method can be effectively applied to the fault diagnosis of gear system.
ISSN:1004-2539