基于VMD和SVDD的滚动轴承早期微弱故障检测和性能退化评估研究

Abstract
Figure/Table
References (21)
Related Citation (0)

Download: PDF (1627 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In order to detect the early weak faults and monitor the fault condition of rolling bearings, a performance degradation evaluation model based on variational mode decomposition (VMD) and support vector data description (SVDD) was proposed. The vibration signal was decomposed by VMD, and the intrinsic mode component which was sensitive to the performance degradation was selected to extract its singular value.Combining the singular value with the time domain feature and the complexity feature vector matrix, a comprehensive feature index of the rolling bearing was coustituted. A SVDD evaluation model was constructed taking the comprehensive characteristics of the normal state of the bearing, as the training sample and the rolling bearing’s whole life test data were used to verify the degradation assessment model. The experimental results show that the evaluation model can accurately detect the early stage weak failure of rolling bearings. At the same time, the performance degradation assessment of rolling bearings can be effectively achieved. The evaluation effect is superior to that of the fuzzy C-means clustering (FCM) method.

Key words： Rolling bearing weak faults performance degradation VMD SVDD;

Received: 07 May 2018 Published: 15 November 2019

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WANG Fei1
	FANG Liqing2
	ZHAO Yulong2
	QI Ziyuan2

Cite this article:

WANG Fei1,FANG Liqing2,ZHAO Yulong2, et al. Rolling bearing early weak fault detection and performance degradation assessment based on VMD and SVDD[J]. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(22): 224-230.

URL:

http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2019/V38/I22/224

[1] Yuning qian, Ruqiang Yan, Robert X. A multi-time scale approach to remaining useful life prediction in rolling bearing [J]. Mechanical Systems and Signal Processing, 2017, 83(6): 549-567.
[2] 潘玉娜. 滚动轴承的性能退化特征提取及评估方法研究[D]. 上海: 上海交通大学, 2011.
Pan Yuna. Study on Feature Extraction and Assessment Method of Rolling Element Bearing Performance Degradation[D].Shanghai: Shanghai Jiaotong University,2011.
[3] 潘玉娜,陈进,李兴林. 基于模糊c-均值的设备性能退化评估方法[J]. 上海交通大学学报, 2009, 43(11): 1794-1797.
Pan Yuna,Chen Jin,Li Xinglin. Fuzzy c-Means Based Equipment Performance Degradation Assessment[J].Journal of Shanghai Jiaotong University,2009,43(11):1794-1797.
[4] Jianbo Yu. Bearing performance degradation assessment using locality preserving projections and Gaussian mix[J]. Mechanical Systems and Signal Processing, 2011, 25(2): 2573-2589.
[5] 刘韬. 基于隐马尔可夫模型与信息融合的设备故障诊断与性能退化评估研究[D]. 上海: 上海交通大学, 2013.
Liu Tao. Study of Hidden Markov Model and Information Fusion in Equipment Fault Diagnosis and Performance Degradation Assessment[D]. Shanghai: Shanghai Jiaotong University,2013.
[6] Akhand rai, S.H.Upadhyay. Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering[J]. Mechanical Systems and Signal Processing, 2017, 93(5): 16-29.
[7] Dominique zosso Konstantin-Dragomiretskiy. Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2013, 62(10): 531-544.
[8] 岳应娟,孙钢,蔡艳平. 内燃机KVMD_MHD振动谱图表征与TD_2DPCA编码诊断方法研究[J]. 振动工程学报, 2017, 30(4): 688-696.
Yue Yingjuan,Sungang,Cai Yanping. KVMD-MHD vibration spectrum representation of internal combustionengine and TI-2DPCA coding diagnosis method[J]. Journal of Vibration on Engineering,2017, 30(4): 688-696.
[9] 钱林,康敏,傅秀清. 基于VMD的自适应形态学在轴承故障诊断中的应用[J]. 振动与冲击, 2017, 36(3): 227-233.
Qian Lin,Kang Min,Fu Xiu Qing. VMD based adaptive composite multiscale fuzzy entropy and itsapplication to fault diagnosis of rolling bearing[J]. Journal of Vibration and Shock, 2017, 36(3): 227-233.
[10]Yuanyuan liu, Gongliu Yang. Variational mode decomposition denoising combined the detrended fluctuation analysis [J]. Signal Processing, 2016, 125(2): 349-364.
[11]Aneesh Sachin-Kumar. Performance Comparison of Variational Mode Decomposition over Empirical Wavelet Transform for the Classification of Power Quality Disturbances Using Support Vector Machine [J]. Procedia Computer Science, 2015, 46(3): 372-380.
[12] Ming zhang, Zhinong Jian,Kun Feng. Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump [J]. Mechanical Systems and Signal Processing, 2017, 93(2): 460-493.
[13] Yanxue wang, Richard Markert. Research on variational mode decomposition and its application in detecting rub impact fault of the rotor system [J]. Mechanical Systems and Signal Processing, 2015, 61(3): 241-253.
[14] D.m.j. tax ,R.P.W.Duin. Support vector domain description,[J]. Pattern Recognition, 1999, 20(6): 1191-1199.
[15] Xiaoran zhu,Youyun Zhang,Yongsheng Zhu. Bearing performance degradation assessment based on the rough support vector data description [J]. Mechanical Systems and Signal Processing, 2013, 34(9): 203-217.
[16] 周胜明,曲建岭,高峰. 基于HE_SVDD的航空发动机工作状态识别[J]. 仪器仪表学报, 2016, 37(2): 308-315.
Zhou Shengming,Qu Jianling,Gao Feng.Aero-engine working condition recognition based on HE-SVDD[J]. Chinese Journal of Scientific Instrument, 2016, 37(2): 308-315.
[17] 李兆飞,柴毅,任小洪. 混沌分形特征与支持向量数据域描述辨识机械动态系统异常[J]. 农业工程学报, 2015, 31(10): 211-217.
Li Zhaofei, Chai Yi, Ren Xiaohong. Abnormal identification method of dynamic systems based on support vector data description feature extraction for chaos fractal[J]. Transactions of the Chinese Society of Agricultural Engineering , 2015, 31(10): 211-218.
[18] 刘志亮,刘仕林,李兴林. 滚动轴承安全域建模方法及其在高速列车异常检测中的应用[J]. 机械工程学报, 2017, 53(10): 116-124.
Liu Zhiliang,Liu Shilin,Li Xinglin.Safety Domain Modelling of Rolling Bearings and Its Application to Anomaly Detection for High-speed Rail Vehicles[J].Journal of Mechanical Engineering, 2017, 53(10): 116-124.
[19] 赵志宏,杨绍普. 一种基于样本熵的轴承故障诊断方法[J]. 振动与冲击, 2012, 31(6): 138-142.
Zhao Zhihong,Yang Shaopu.Sample entropy-based roller bearing fault diagnosis method[J]. Journal of Vibration and Shock, 2012, 31(6): 138-142.
[20] 周建民,徐清瑶,张龙,等. 结合小波包奇异谱熵和SVDD的滚动轴承性能退化评估[J]. 机械科学与技术, 2016, 35(12): 1882-1887.
Zhou Jianmin,Xu Qingyao,Zhang Long.Assessment Method of Rolling Bearing Performance Degradation basedon Wavelet Packet Singular Spectral Entropy and SVDD[J].Mechanical Science and Technology for Aerospace Engineering, 2016, 35(12): 1882-1887.
[21] 朱朔,白瑞林,刘秦川. 基于果蝇优化算法-小波支持向量数据描述的滚动轴承性能退化评估[J]. 中国机械工程, 2018, 29(5): 602-608.
Zhu Suo,Bai Ruilin,Liu Qinchuan. Rolling Bearing Performance Degradation Assessment Based on FOA-WSVDD[J].China Mechanical Engineering, 2018, 29(5): 602-608.