基于IVMD和马田系统的滚动轴承故障检测方法

doi:10.3969/j.issn.1000-1158.2019.06.23

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Abstract Aiming at the sensitivity and reliability of the single characteristic parameters of rolling bearing operation signal to early faults, a rolling bearing fault diagnosis method based on IVMD and Mahalanobis-Taguchi system was proposed. The method firstly determined the number of layers of the variational modal decomposition based on the spectrum correlation coefficient; and secondly, the modified variational mode decomposition was used to decompose the mechanical vibration signal to obtain a series of band-limited intrinsic mode functions, and calculated the characteristic statistical components of each modal component. Then, the base space of the MTS system on this basis was built. The SNR method was used to screen effective feature variables, and the benchmark space of MTS system was rebuilt. Finally, the Mahalanobis distance from the diagnostic signal to the reference space was calculated to detect the initial fault, and the diagnostic control index of the early fault of the rolling bearing was established.

Key words： metrology IVMD rolling bearings mahalanobis-taguchi system fault diagnosis

Received: 23 November 2018 Published: 10 October 2019

PACS:

TB936

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	Articles by authors
	CHEN Jian
	ZHUANG Xue-kai
	LV Wu-yang
	TAO Shan-yong
	WANG Wei

Cite this article:

CHEN Jian,ZHUANG Xue-kai,LV Wu-yang, et al. Fault Diagnosis of Rolling Bearing Using Mahalanobis-Taguchi System Based on IVMD[J]. Acta Metrologica Sinica, 2019, 40(6): 1083-1087.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.06.23 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I6/1083

［1］Ali J B, Fnaiech N, Saidi L, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals ［J］. Applied Acoustics, 2015, 89: 16-27.
［2］马增强, 李亚超, 刘政, 等. 基于变分模态分解和Teager能量算子的滚动轴承故障特征提取［J］. 振动与冲击, 2016, 35(13): 134-139.
Ma Z Q, Li Y C, Liu Z, et al. Rolling bearingsfault feature extraction based on variational mode decomposition and Teager energy operator ［J］. Shock and Vibration, 2016, 35(13): 134-139.
［3］张云强, 张培林, 王怀光, 等. 结合VMD和Volterra预测模型的轴承振动信号特征提取［J］. 振动与冲击, 2018, 37(3): 129-135, 152.
Zhang Y Q, Zhang P L, Wang H G, et al. Feature extraction method for rolling bearing vibration signals based on VMD and Volterra prediction model ［J］. Shock and Vibration, 2018, 37(3): 129-135, 152.
［4］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 X X, Zhou G W, Ren H H, et al. A rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy ［J］. Shock and Vibration, 2017, 36(22): 22-28.
［5］Shen C, Wang D, Kong F, et al. Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier ［J］. Measurement, 2013, 46(4): 1551-1564.
［6］Ao H L, Cheng J, Li K, et al. A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM ［J］. Shock and Vibration, 2014: Article ID 825825.
［7］Liu Z, Cao H, Chen X, et al. Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings ［J］. Neuro computing, 2013, 99(1): 399-410.
［8］张淑清,邢婷婷,何红梅,等. 基于VMD及广义分形维数矩阵的滚动轴承故障诊断［J］. 计量学报,2017,38(4): 439-443.
Zhang S Q, Xing T T, He H M, et al. Bearing Fault Diagnosis Method Based on VMD and Generalized
Fractal Dimension Matrix［J］. Acta Metrologica Sinica, 2017, 38(4): 439-443.
［9］付秀伟,高兴泉. 基于傅里叶分解与奇异值差分谱的滚动轴承故障诊断方法［J］. 计量学报,2018,39(5): 688-692.
Fu X W, Gao X Q. Rolling Bearing Fault Diagnosis Based on FDM and Singular Value Difference Spectrum ［J］. Acta Metrologica Sinica, 2018, 39(5): 688-692.

［10］Hu J, Zhang L, Liang W, et al. Incipient mechanical fault detection based on multifractal and MTS methods ［J］. Petroleum Science, 2009, 6(2): 208-216.
［11］Wang Z, Lu C, Wang Z L, et al. Fault Diagnosis and health assessment for bearings using the Mahalanobis-Taguchi system based on EMD-SVD ［J］. Transactions of the Institute of Measurement and Control, 2013, 35(6): 798-807.
［12］剡昌锋, 朱涛, 吴黎晓, 等. 基于马田系统的滚动轴承初始故障检测和状态监测［J］. 振动与冲击, 2017, 36(12): 155-162, 188.
Yan C F, Zhu T, Wu L X, et al. Incipient fault detection and condition monitoring of rolling bearings by using the Mahalanobis-Taguchi System ［J］. Shock and Vibration, 2017, 36(12): 155-162, 188.
［13］陈俊洵, 程龙生, 胡绍林, 等. 基于EMD的改进马田系统的滚动轴承故障诊断［J］. 振动与冲击, 2017, 36(5): 151-156.
Chen J X, Cheng L S, Hu S L, et al. Fault diagnosis of rolling bearings using modified Mahalanobis-Taguchi system based on EMD ［J］. Shock and Vibration, 2017, 36(5): 151-156.
［14］陈俊洵, 程龙生, 余慧, 等. 基于EMD-SVD与马田系统的复杂系统健康状态评估［J］. 系统工程与电子技术, 2017, 39(7): 1542-1548.
Chen J X, Cheng L S, Yu H, et al. Health status assessment for complex systems based on EMD-SVD and Mahalanobis-Taguchi system ［J］. Systems Engineering and Electronics, 2017, 39(7): 1542-1548.