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Rolling Element Bearing Fault Diagnosis by Combining Adaptive Local Iterative Filtering Modified Fuzzy Entropy and Support Vector Machine

机译:通过组合自适应局部迭代过滤改进的模糊熵和支持向量机滚动元件轴承故障诊断

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摘要

A new fault feature extraction method for rolling element bearing is put forward in this paper based on the adaptive local iterative filtering (ALIF) algorithm and the modified fuzzy entropy. Due to the bearing vibration signals’ non-stationary and nonlinear characteristics, the ALIF method, which is a new approach for the analysis of the non-stationary signals, is used to decompose the original vibration signals into a series of mode components. Fuzzy entropy (FuzzyEn) is a nonlinear dynamic parameter for measuring the signals’ complexity. However, it only emphasizes the signals’ local characteristics while neglecting its global fluctuation. Considering the global fluctuation of bearing vibration signals will change with the bearing working condition varying, we modified the FuzzyEn. The modified FuzzyEn (MFuzzyEn) of the first few modes obtained by the ALIF is utilized to form the fault feature vectors. Subsequently, the corresponding feature vectors are input into the multi-class SVM classifier to accomplish the bearing fault identification automatically. The experimental analysis demonstrates that the presented ALIF-MFuzzyEn-SVM approach can effectively recognize the different fault categories and different levels of bearing fault severity.
机译:基于自适应局部迭代滤波(ALIF)算法和改进的模糊熵,本文提出了一种用于滚动元件轴承的新故障特征提取方法。由于轴承振动信号的非静止和非线性特性,是用于分析非静止信号的新方法的ALIF方法,用于将原始振动信号分解为一系列模式分量。模糊熵(Fuzzyen)是用于测量信号“复杂性的非线性动态参数。然而,它仅在忽略其全球波动的同时强调信号“局部特征。考虑到轴承振动信号的全局波动将随着轴承工作条件改变而变化,我们修改了模糊。通过ALIF获得的前几种模式的改性模糊(MFUZZYEN)用于形成故障特征向量。随后,将相应的特征向量输入到多级SVM分类器中以自动完成轴承故障识别。实验分析表明,所呈现的Alif-MFuzzyen-SVM方法可以有效地识别不同的故障类别和不同轴承故障严重程度。

著录项

  • 期刊名称 Entropy
  • 作者

    Keheng Zhu; Liang Chen; Xiong Hu;

  • 作者单位
  • 年(卷),期 2018(20),12
  • 年度 2018
  • 页码 926
  • 总页数 12
  • 原文格式 PDF
  • 正文语种
  • 中图分类
  • 关键词

    机译:自适应局部迭代过滤;改性模糊熵;SVM;滚动元件轴承;故障诊断;

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