基于多尺度均值排列熵和参数优化支持向量机的轴承故障诊断

Abstract
Figure/Table
References (21)
Related Citation (15)

Download: PDF (3147 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Aiming at the problems of feature extraction and low accuracy of pattern recognition in rolling bearing fault diagnosis, a fault diagnosis method based on multi-scale mean permutation entropy (MMPE) and grey wolf optimized support vector machine (GWO-SVM) was proposed. Firstly, the MMPE was applied to comprehensively characterize rolling bearing fault feature information. Then, the appropriate dimension features were selected to form the sample data set. Finally, GWO-SVM classifier was employed for fault pattern recognition. In this paper, the proposed fault diagnosis method based on MMPE and GWO-SVM was theoretically analyzed and studied, and the corresponding comparative experimental analysis was carried out by using the experimental data of rolling bearing, and the results showed that: MMPE can effectively extract the fault feature information of rolling bearing; the recognition accuracy and recognition speed of GWO-SVM are better than those of other commonly used parameters optimization SVM methods of rolling bearing fault diagnosis; the proposed method can effectively identify the fault position and fault degree of rolling bearing, and the fault recognition accuracy is 98.0% on the rolling bearing data set, which is higher than 97.0% based on MPE and GWO-SVM, furthermore, the recognition accuracy is 93.5% under the background of noise, while the accuracy of the latter is only 83.0% under the same conditions, it proves that MMPE has better noise robustness.

Key words： Rolling Bearing Fault Diagnosis Multi-scale Mean Permutation Entropy Grey Wolf Optimization Support Vector Machine

Received: 16 September 2020 Published: 15 January 2022

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WANG Gongxian
	ZHANG Miao
	HU Zhihui
	XIANG Lei
	ZHAO Bokun

Cite this article:

WANG Gongxian,ZHANG Miao,HU Zhihui, et al. Bearing fault diagnosis based on multi-scale mean permutation entropy and parametric optimization SVM[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(1): 221-228.

URL:

http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I1/221

[1] 唐贵基, 庞彬, 刘尚坤. 基于奇异差分谱和平稳子空间分析的滚动轴承故障诊断[J]. 振动与冲击, 2015, 34(11): 83-87+115.
TANG Guiji, PANG Bin, LIU Shangkun. Fault diagnosis of rolling bearings based on difference spectrum of singular value and stationary subspace analysis[J]. Journal of Vibration and Shock, 2015, 34(11): 83-87+115.
[2] 郑近德, 刘涛, 孟瑞, 等. 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.
ZHEN Jinde, LIU Tao, MENG Rui, et al. Generalized composite multiscale permutation entropy and PCA based fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2018, 37(20): 61-66.
[3] ZHANG X, LIANG Y, ZHOU J. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM[J]. Measurement, 2015, 69: 164-179.
[4] 李学军, 何能胜, 何宽芳, 等. 基于小波包近似熵和SVM的圆柱滚子轴承诊断[J]. 振动、测试与诊断, 2015，35(06): 1031-1036+1196-1197.
LI Xuejun, HE Nengsheng, HE Kuanfang, et al. Cylindrical roller bearing diagnosis based on wavelet packet approximate entropy and support vector machines[J]. Journal of Vibration, Measurement & Diagnosis, 2015，35(06): 1031-1036+1196-1197.
[5] LIU X, CHEN Y, YANG J. A novel fault diagnosis method for rolling bearing based on EEMD-PE and multiclass relevance vector machine[C]//2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2017: 1-6.
[6] 戴邵武, 陈强强, 戴洪德, 等. 基于平滑先验分析和模糊熵的滚动轴承故障诊断[J]. 航空动力学报, 2019, 34(10): 2218-2226.
DAI Shaowu, CHEN Qiangqiang, DAI Hongde, et al. Rolling bearing fault diagnosis based on smoothness priors approach and fuzzy entropy[J]. Journal of Aerospace Power, 2019, 34(10): 2218-2226.
[7] LI Y, XU M, WEI Y, et al. A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree[J]. Measurement, 2016, 77: 80-94.
[8] BANDT C, POMPE B. Permutation entropy: a natural complexity measure for time series[J]. Physical review letters, 2002, 88(17): 174102.
[9] HUO Z, ZHANG Y, SHU L, et al. A new bearing fault diagnosis method based on fine-to-coarse multiscale permutation entropy, laplacian score and SVM[J]. IEEE Access, 2019, 7: 17050-17066.
[10] 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(05): 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 & Diagnosis, 2014, 34(05): 802-806+971.
[11] 刘晓东, 刘朦月, 陈寅生, 等. EEMD-PE与M-RVM相结合的轴承故障诊断方法[J]. 哈尔滨工业大学学报, 2017, 49(09): 122-128.
LIU Xiaodong, LIU Mengyue, CHEN Yinsheng, et al. Rolling bearing fault diagnosis based on EEMD-PE coupled with M-RVM[J]. Journal of Harbin Institute of Technology, 2017, 49(09): 122-128.
[12] 郑小霞, 周国旺, 任浩翰, 等. 基于变分模态分解和排列熵的滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(22): 22-28.
ZHENG Xiaoxia, ZHOU Guowang, REN Haohan, et al. A rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy[J]. Journal of Vibration and Shock, 2017, 36(22): 22-28.
[13] AZIZ W, ARIF M. Multiscale permutation entropy of physiological time series[C]//2005 Pakistan Section Multitopic Conference. IEEE, 2005: 1-6.
[14] 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(19): 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(19): 2641-2646.
[15] 姚成玉, 来博文, 陈东宁, 等. 基于最小熵解卷积-变分模态分解和优化支持向量机的滚动轴承故障诊断方法[J]. 中国机械工程, 2017, 28(24): 3001-3012.
YAO Chengyu, LAI Bowen, CHEN Dongning, et al. Fault diagnosis method based on MED-VMD and optimized SVM for rolling bearings[J]. China Mechanical Engineering, 2017, 28(24): 3001-3012.
[16] LI Y, GAO Y, GUO J, et al. fault diagnosis of metallurgical machinery based on Spectral Kurtosis and GA-SVM[C]//Advanced Materials Research. Trans Tech Publications Ltd, 2013, 634: 3958-3961.
[17] YAN X, JIA M. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing[J]. Neurocomputing, 2018, 313: 47-64.
[18] 姚德臣, 杨建伟, 程晓卿, 等. 基于多尺度本征模态排列熵和SA-SVM的轴承故障诊断研究[J]. 机械工程学报, 2018, 54(09): 168-176.
YAO Dechen, YANG Jianwei, CHENG Xiaoqin, et al. Railway rolling bearing fault diagnosis based on multi-scale IMF permutation entropy and SA-SVM classifier[J]. Journal of Mechanical Engineering, 2018, 54(09): 168-176.
[19] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.
[20] 宋宣毅, 刘月田, 马晶, 等. 基于灰狼算法优化的支持向量机产能预测[J]. 岩性油气藏, 2020, 32(02): 134-140.
SONG Xuanyi, LIU Yuetian, MA Jing et al. Productivity forecast based on support vector machine optimized by grey wolf optimizer[J]. Lithologic Reservoirs, 2020, 32(02): 134-140.
[21] DONG Z, ZHENG J, HUANG S, et al. Time-shift multi-scale weighted permutation entropy and GWO-SVM based fault diagnosis approach for rolling bearing[J]. Entropy, 2019, 21(6): 621.

[1]	. [J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(2): 20-25.