FAULT DIAGNOSIS METHOD BASED ON LCD AND MULTIFRACTAL DETREDED FLUCTUATION ANALYSIS

YANG Le

doi:10.16579/j.issn.1001.9669.2018.03.009

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FAULT DIAGNOSIS METHOD BASED ON LCD AND MULTIFRACTAL DETREDED FLUCTUATION ANALYSIS

更新时间：2022-09-22

- FAULT DIAGNOSIS METHOD BASED ON LCD AND MULTIFRACTAL DETREDED FLUCTUATION ANALYSIS
- Journal of Mechanical Strength Vol. 40, Issue 3, Pages: 559-564(2018)
- 作者机构：
  
  1. 重庆工业职业技术学院自动化学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2018.03.009
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YANG Le. FAULT DIAGNOSIS METHOD BASED ON LCD AND MULTIFRACTAL DETREDED FLUCTUATION ANALYSIS. [J]. 40(3):559-564(2018)
DOI：

YANG Le. FAULT DIAGNOSIS METHOD BASED ON LCD AND MULTIFRACTAL DETREDED FLUCTUATION ANALYSIS. [J]. 40(3):559-564(2018) DOI： 10.16579/j.issn.1001.9669.2018.03.009.

摘要

针对滚动轴承振动信号非线性、非平稳性以及故障特征难以提取的问题,提出了基于局部特征尺度分解(local characteristic-scale decomposition,LCD)和多重分形去趋势波动分析(multifractal detrended fluctuation analysis,MFDFA)的故障诊断方法。该方法首先利用LCD将振动信号分解成不同尺度下的内禀尺度分量(intrinsic scale component,ISC)。其次,对包含主要信息的前几个ISC分量进行MF-DFA分析,并选取每个ISC分量的Hurst指数作为故障特征。然后,采用线性局部切空间排列(liner local tangent space alignment,LLTSA)对故障特征进行降维以获得对故障敏感的低维特征。最后,利用支持向量机(support vector machine,SVM)对提取特征进行分类识别。滚动轴承的故障诊断实验表明,所提方法能够有效地识别滚动轴承的典型故障,具有一定的优势。

Abstract

Aiming at the fact that the vibration signal of rolling bearing would exactly display non-stationary characteristics and fault features hard to extracted,a fault method of rolling bearing based on local characteristic-scale decomposition（ LCD）and multifractal detrended fluctuation analysis（ MF-DFA） was proposed. Firstly,the vibration signals was decomposed into several intrinsic scale components（ ISC）. Secondly,the intrinsic features hidden in each major ISC were extracted by using MFDFA,among which the generalized Hurst exponents are selected as fault feature. Thirdly,liner local tangent space alignment（ LLTSA） was applied to compress the high-dimension features into low-dimension features which insensitive to fault. Finally,the support vector machine（ SVM） was employed to diagnosis fault. Experiment results of rolling bearing show that the proposed method can classify typical fault of rolling bearing exactly and has certain superiority.

关键词

局部特征尺度分解多重分形去趋势波动分析特征提取滚动轴承故障诊断

Keywords

Local characteristic-scale DecompositionMultifractal detrended fluctuation analysisFeature extractionRolling bearingFault diagnosis

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