基于广义复合多尺度排列熵与PCA的滚动轴承故障诊断方法

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (20) : 61-66.

论文

郑近德，刘涛，孟瑞，刘庆运

作者信息 +

Generalized composite multiscale permutation entropy and PCA based fault diagnosis of rolling bearings

ZHENG Jinde,LIU Tao,MENG Rui,LIU Qingyun

Author information +

文章历史 +

摘要

多尺度排列熵能够有效地反映滚动轴承振动信号的随机性变化和非线性动力学突变行为。针对其多尺度过程中粗粒化方式的不足，提出了广义复合多尺度排列熵（Generalized Composite Multiscale Permutation Entropy，GCMPE）。研究了参数对GCMPE计算的影响，并通过分析仿真数据将GCMPE与MPE进行了对比。将GCMPE应用于滚动轴承非线性故障特征的提取，提出一种基于GCMPE、主元分析和支持向量机的滚动轴承智能故障诊断方法。将提出的方法应用于实验数据分析，结果表明，所提方法能够有效地实现滚动轴承故障诊断，且故障识别率较高。

Abstract

Multiscale permutation entropy (MPE) can effectively extract the nonlinear dynamic fault feature from vibration signals of rolling bearings.Aiming at the problem of coarse-graining in MPE, a new nonlinear dynamic method called generalized composite multiscale permutation entropy (GCMPE) was proposed.GCMPE was compared with the MPE by analyzing simulation data and also the influence of parameters on GCMPE calculation was studied.Then GCMPE was applied to the extraction of nonlinear fault feature from vibration signal of rolling bearings and a new rolling bearing fault diagnosis method based on GCMPE, principal component analysis and support vector machine was presented.Finally, the proposed method was applied to analyze experimental data of rolling bearing and the results show that the proposed method can effectively realize the fault diagnosis of rolling bearings and has a higher fault recognition rate.

导出引用

郑近德，刘涛，孟瑞，刘庆运. 基于广义复合多尺度排列熵与PCA的滚动轴承故障诊断方法[J]. 振动与冲击, 2018, 37(20): 61-66

ZHENG Jinde,LIU Tao,MENG Rui,LIU Qingyun. Generalized composite multiscale permutation entropy and PCA based fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2018, 37(20): 61-66

参考文献

[1] YAN Ruqiang, GAO R X. Approximate entropy as a diagnostic tool for machine health monitoring [J]. Mechanical Systems and Signal Processing, 2007, 21 (2): 824-839.
[2] 石博强, 申焱华. 机械故障诊断的分形方法: 理论与实践[M]. 冶金工业出版社, 2001.
[3] 胥永刚, 李凌均. 近似熵及其在机械设备故障诊断中的应用[J]. 信息与控制, 2002, 31 (6): 547-551.
XU Yonggang, LI Lingjun. Approximate entropy and its applications in mechanical fault diagnosis[J]. Information and Control, 2002, 31 (6): 547-551.
[4] 胥永刚, 何正嘉. 分形维数和近似熵用于度量信号复杂性的比较研究[J]. 振动与冲击, 2003, 22 (3): 25-27.
XU Yonggang, HE Zhengjia. Research on comparison between approximate entropy and fractal dimension for complexity measure of signals[J]. Journal of Vibration and Shook, 2003, 22 (3): 25-27.
[5] YAN Ruqiang, LIU Yongbin, GAO R X. Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines [J]. Mechanical Systems and Signal Processing, 2012, 29: 474-484.
[6] 郑近德，程军圣，杨宇. 基于LCD和排列熵的滚动轴承故障诊断[J]. 振动测试与诊断，2014, 34 (5): 802-806+971.
ZHENG Jinde, CHENG Junsheng, YANG Yu. A Rolling Bearing Fault Diagnosis Method Based on LCD and Permutation Entropy[J]. Journal of Vibration, Measurement and Diagnosis, 2014, 34 (5): 802-806+971.
[7] 郑近德, 程军圣, 杨宇. 多尺度排列熵及其在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2013, 24 (019): 2641-2646.
ZHENG Jinde CHENG Junsheng YANG Yu. Multi-scale Permutation Entropy and Its Applications to Rolling Bearing Fault Diagnosis[J]. China Mechanical Engineering, 2013, 24 (019): 2641-2646.
[8] WU S D, WU C W, LIN S G, et al. Time series analysis using composite multi-scale entropy[J]. Entropy, 2013, 15 (3): 1069-1084.
[9] COSTA M, GOLDBERGER A. Generalized multiscale entropy analysis: application to quantifying the complex volatility of human heartbeat time series[J]. Entropy, 2015, 17(3): 1197-1203.
[10] 徐卓飞, 刘凯, 张海燕,等. 基于经验模式分解和主元分析的滚动轴承故障诊断方法研究[J]. 振动与冲击, 2014, 33(23):133-139.
Xu Zhuofei, LIU Kai, Zhang Haiyan, et al. A Fault diagnosis method for Rolling Bearing based on Empirical Mode Decomposition and Principal Component Analysis[J]. Jounal of Vibration and Shock , 2014, 33(23): 133-139.
[11] 王建国，陈帅，张超. 噪声参数最优ELMD与LS-SVM在轴承故障诊断中的应用与研究[J]. 振动与冲击, 2017, 36(5): 72-78.
Wang Jian-guo, Chen Shua, Zhang Chao. Application and research of optimal noise parameters ensemble local mean decomposition and LS-SVM in bearing fault diagnosis. Journal of Vibration and Shock, 2017, 36(5): 72-78.
[12] 段礼祥，郭晗，王金江. 数据集不均衡下的设备故障程度识别方法研究[J]. 振动与冲击, 2016, 35(20): 178-182.
DUAN Li-xiang, GUO Han, WANG Jin-jiang.. Mechanical fault Severity Identification Methods under Unbalanced Datasets. Journal of Vibration and Shock, 2016, 35(20): 178-182.
[13] AZIZ W, ARIF M. Multiscale permutation entropy of physiological time series[C]//9th International Multitopic Conference, IEEE INMIC 2005. IEEE, 2005: 1-6.
[14] BANDT C, POMPE B. Permutation entropy: a natural complexity measure for time series[J]. Physical Review Letters, 2002, 88(17): 174102.
[15] http://www.csie.ntu.edu.tw/~cjlin/libsvm/