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

Wenbin Zhang; Jie Jiang; Yasong Pu; Libin Yu; Dewei Guo; Jie Min

doi:10.16578/j.issn.1004.2539.2021.05.022

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

Development·Application | 更新时间：2022-11-10

- Application of Adaptive Local Iterative Filtering and Fuzzy Entropy in Gear Fault Identification
- Journal of Mechanical Transmission Vol. 45, Issue 5, Pages: 146-152(2021)
- 作者机构：
  
  1.红河学院工学院，云南省高校高原机械性能分析与优化重点实验室，云南蒙自 661199
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2021.05.022
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张文斌,江洁,普亚松等.自适应局部迭代滤波与模糊熵在齿轮系统故障识别中的应用[J].机械传动,2021,45(05):146-152.

Zhang Wenbin,Jiang Jie,Pu Yasong,et al.Application of Adaptive Local Iterative Filtering and Fuzzy Entropy in Gear Fault Identification[J].Journal of Mechanical Transmission,2021,45(05):146-152.
张文斌,江洁,普亚松等.自适应局部迭代滤波与模糊熵在齿轮系统故障识别中的应用[J].机械传动,2021,45(05):146-152. DOI： 10.16578/j.issn.1004.2539.2021.05.022.

Zhang Wenbin,Jiang Jie,Pu Yasong,et al.Application of Adaptive Local Iterative Filtering and Fuzzy Entropy in Gear Fault Identification[J].Journal of Mechanical Transmission,2021,45(05):146-152. DOI： 10.16578/j.issn.1004.2539.2021.05.022.

摘要

针对齿轮系统实测信号受噪声干扰而不能准确反映故障特征的问题，提出了一种自适应局部迭代滤波与模糊熵相结合的故障识别方法。利用自适应局部迭代滤波可以将齿轮非平稳信号分解为有限个平稳的本质模态函数，由于自适应局部迭代滤波能有效分离出齿轮系统的转频信号，因此，以转频信号对应的本质模态函数为分界，计算前几个本质模态函数的模糊熵，最后，通过计算不同工况振动信号模糊熵的灰色关联度来识别齿轮系统不同的故障类型。结果表明，该方法能够有效地应用于齿轮系统的故障诊断。

Abstract

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.

关键词

故障分析齿轮信号处理自适应局部迭代滤波模糊熵

Keywords

Fault analysisGearSignal processingAdaptive local iterative filteringFuzzy entropy

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