Bearing fault diagnosis algorithm based on maximum correlated kurtosis feature mode decomposition and compound Gini index

YANG Gang; XU Wuyi; DENG Qin; QIN Limu; WEI Yuqian

doi:10.13890/j.issn.1000-128X.2023.04.002

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Bearing fault diagnosis algorithm based on maximum correlated kurtosis feature mode decomposition and compound Gini index

Railway Rolling Stock | 更新时间：2024-08-02

- Bearing fault diagnosis algorithm based on maximum correlated kurtosis feature mode decomposition and compound Gini index
- Electric Drive for Locomotives Issue 4, Pages: 9-17(2023)
- 作者机构：
  
  1.西南交通大学机械工程学院，四川成都 610031
  2.西南交通大学唐山研究院，河北唐山;063000
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2023.04.002
  CLC： U266.2;U260.331⁺.2
- Published：10 July 2023，
  
  Received：04 May 2023，
- 稿件说明：
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杨岗, 徐五一, 邓琴, 等. 基于复合基尼指数和最大相关峭度特征模态分解的轴承故障诊断算法[J]. 机车电传动, 2023(4): 9-17.

YANG Gang, XU Wuyi, DENG Qin, et al. Bearing fault diagnosis algorithm based on maximum correlated kurtosis feature mode decomposition and compound Gini index[J]. Electric drive for locomotives,2023(4): 9-17.
杨岗, 徐五一, 邓琴, 等. 基于复合基尼指数和最大相关峭度特征模态分解的轴承故障诊断算法[J]. 机车电传动, 2023(4): 9-17. DOI： 10.13890/j.issn.1000-128X.2023.04.002.

YANG Gang, XU Wuyi, DENG Qin, et al. Bearing fault diagnosis algorithm based on maximum correlated kurtosis feature mode decomposition and compound Gini index[J]. Electric drive for locomotives,2023(4): 9-17. DOI： 10.13890/j.issn.1000-128X.2023.04.002.

摘要

最大相关峭度特征模态分解可以有效去除冗余信息，实现故障特征增强，但是其效果受分解模态数量、初始化滤波器个数和滤波器长度的影响。针对此问题，文章提出了一种基于复合基尼指数（Compound Gini Index

CGI）与最大相关峭度特征模态分解（Maximum Correlated Kurtosis Feature Mode Decomposition

MCKFMD）的轴承故障诊断方法。首先，将时域平方基尼指数和频域平方基尼指数结合，构建了一种能够同时量化时域和频域周期性脉冲丰富度的新稀疏测度指标，命名为复合基尼指数，并对其性能特性进行评估验证；其次，使用CGI作为沙丘猫群优化算法（Sand Cat Swarm Optimization

SCSO）寻优的适应度函数，快速准确地得到MCKFMD的最优参数组合，实现故障信号的自适应分解；最后，利用CGI选取最优模态，并进行希尔伯特包络解调，实现故障特征提取。通过仿真信号和试验信号验证了所提方法的有效性。对比性研究表明，与参数优化VMD和固定参数MCKFMD相比，文章所提方法在提取周期性故障特征方面更为有效。

Abstract

The maximum correlated kurtosis feature mode decomposition (MCKFMD) method can effectively remove redundant information and enhance fault features

but its effect is affected by the number of decomposition modes

the number of initialized filters and the filter length. To address this problem

a bearing fault diagnosis method based on compound Gini index (CGI) and MCKFMD was proposed. Firstly

a new sparse index called CGI was constructed by combining the squared Gini index in the time domain and frequency domain to quantify the abundance of periodic impulses in the time domain and frequency domain

and its performance was evaluated and verified. Secondly

CGI was used as the fitness function for the sand cat swarm optimization (SCSO) algorithm to quickly and accurately obtain the optimal parameter combination of MCKFMD and realize the adaptive decomposition of fault signals. Finally

CGI was used to select the optimal mode for Hilbert envelope demodulation to achieve fault feature extraction. The proposed method was verified for effectiveness by using analog and experimental signals

and comparative studies have shown that it is more effective in extracting periodic fault features compared with the parameter-optimized variational modal decomposition (VMD) and fixed-parameter MCKFMD.

关键词

最大相关峭度特征模态分解沙丘猫群优化算法故障诊断轴承故障复合基尼指数动车组

Keywords

maximum correlated kurtosis feature mode decompositionsand cat swarm optimization algorithmfault diagnosisbearing faultscompound Gini indexEMU

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