浏览全部资源
扫码关注微信
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
Bearing fault diagnosis algorithm based on maximum correlated kurtosis feature mode decomposition and compound Gini index
- 机车电传动 2023年第4期 页码：9-17
- 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：
杨 岗（1973—），男，博士，硕士生导师，研究方向为交通设备故障诊断与控制工程、人工智能与大数据、弓网动力学与主动控制；E-mail: yanggang@swjtu.cn
- 基金信息：国家重点研发计划项目(2020YFB1200300ZL);四川省科技计划项目(2022YFG0088)
DOI：10.13890/j.issn.1000-128X.2023.04.002
中图分类号： U266.2;U260.331+.2纸质出版日期：2023-07-10，
收稿日期：2023-05-04，
- 稿件说明：
扫 描 看 全 文
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
references
贺雅, 胡明辉, 卢子元, 等. 基于改进群延迟估计的同步压缩变换及其在冲击类振动信号提取中的应用[J]. 机械工程学报, 2022, 58(4): 22-33.
HE Ya, HU Minghui, LU Ziyuan, et al. Synchrosqueezing transform based on improved group delay estimation and its application in extracting impulse vibration signal[J]. Journal of mechanical engineering, 2022, 58(4): 22-33.
NI Qing, JI J C, FENG Ke, et al. A novel correntropy-based band selection method for the fault diagnosis of bearings under fault-irrelevant impulsive and cyclostationary interferences[J]. Mechanical systems and signal processing, 2021, 153: 107498.
CHENG Yao, WANG Zhiwei, ZHANG Weihua, et al. Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis[J]. ISA transactions, 2019, 90: 244-267.
陈是扦, 彭志科, 周鹏. 信号分解及其在机械故障诊断中的应用研究综述[J]. 机械工程学报, 2020, 56(17): 91-107.
CHEN Shiqian, PENG Zhike, ZHOU Peng. Review of signal decomposition theory and its applications in machine fault diagnosis[J]. Journal of mechanical engineering, 2020, 56(17): 91-107.
HUANG N E, SHEN Zheng, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the royal society of London, Series A: Mathematical, physical and engineering sciences, 1998, 454(1971): 903-995.
WU Zhaohua, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in adaptive data analysis, 2009, 1(1): 1-41.
GILLES J. Empirical wavelet transform[J]. IEEE transactions on signal processing, 2013, 61(16): 3999-4010.
DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
MIAO Yonghao, ZHANG Boyao, LI Chenhui, et al. Feature mode decomposition: new decomposition theory for rotating machinery fault diagnosis[J]. IEEE transactions on industrial electronics, 2023, 70(2): 1949-1960.
刘宏利, 张晓杭, 邵磊, 等. 基于WOA-VMD及综合评价指标的轴承故障诊断[J]. 组合机床与自动化加工技术, 2022(2): 68-71.
LIU Hongli, ZHANG Xiaohang, SHAO Lei, et al. Bearing fault diagnosis based on WOA-VMD and comprehensive evaluation index[J]. Modular machine tool & automatic manufacturing technique, 2022(2): 68-71.
张东兴, 杨岗, 周奥, 等. 基于仿真试验和BOA-VMD的轴箱轴承故障特征提取算法研究[J]. 机车电传动, 2022(2): 105-112.
ZHANG Dongxing, YANG Gang, ZHOU Ao, et al. Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD[J]. Electric drive for locomotives, 2022(2): 105-112.
WANG Dong. Spectral L2/L1 norm: a new perspective for spectral kurtosis for characterizing non-stationary signals[J]. Mechanical systems and signal processing, 2018, 104: 290-293.
WANG Dong. Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients[J]. Mechanical systems and signal processing, 2018, 108: 360-368.
WANG Dong, PENG Zhike, XI Lifeng. The sum of weighted normalized square envelope: a unified framework for kurtosis, negative entropy, Gini index and smoothness index for machine health monitoring[J]. Mechanical systems and signal processing, 2020, 140: 106725.
WANG Dong, PENG Zhike, XI Lifeng. Theoretical and experimental investigations on spectral Lp/Lq norm ratio and spectral Gini index for rotating machine health monitoring[J]. IEEE transactions on automation science and engineering, 2021, 18(3): 1074-1086.
WANG Dong, ZHONG Jingjing, LI Chuan, et al. Box-Cox sparse measures: a new family of sparse measures constructed from kurtosis and negative entropy[J]. Mechanical systems and signal processing, 2021, 160: 107930.
MIAO Yonghao, WANG Jingjing, ZHANG Boyao, et al. Practical framework of Gini index in the application of machinery fault feature extraction[J]. Mechanical systems and signal processing, 2022, 165: 108333.
钱门贵, 陈涛, 于耀翔, 等. 一种改进基尼指数加权的轴承健康指标构建方法[J/OL]. 中国机械工程: 1-7. (2022-09-08) [2023-02-12]. http://kns.cnki.net/kcms/detail/42.1294.th.20220907.1021.006.htmlhttp://kns.cnki.net/kcms/detail/42.1294.th.20220907.1021.006.html.
QIAN Mengui, CHEN Tao, YU Yaoxiang, et al. A method for constructing bearing health indicators with improved Gini index weighting[J/OL]. China mechanical engineering: 1-7. (2022-09-08) [2023-02-12]. http://kns.cnki.net/kcms/detail/42.1294.th.20220907.1021.006.htmlhttp://kns.cnki.net/kcms/detail/42.1294.th.20220907.1021.006.html.
SEYYEDABBASI A, KIANI F. Sand cat swarm optimization: a nature-inspired algorithm to solve global optimization problems[J]. Engineering with computers, 2023, 39(4): 2627-2651.
0
浏览量
28
下载量
0
CSCD
0
CNKI被引量
- 网络PDF下载
- WORD下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
- 技术支持由北京北大方正电子有限公司提供 湘ICP备20010475号-1
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies