An improved TVD fault feature extraction method for motor bearing

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (10) : 203-214.

WANG Fan1,MA Jun1,2,WANG Xiaodong1,2,ZHU Jiangyan1

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Abstract

In order to solve the problem that the use of L1 parametrization in the process of signal feature recovery by Total Variation Denoising (TVD) method leads to the reduction of signal amplitude and the difficulty of selecting the regularization parameter λ, a method of motor bearing fault feature extraction based on No Convex Total Variation Denoising (NCTVD) and Beetle Antennae Search (BAS) is proposed. First, the regularization term in the second-order TVD is defined by introducing an inverse tangent nonconvex penalty function to enhance the impact characteristics of the signal and induce sparsity; second, the BAS algorithm is used to optimize the regularization parameter λ and the convexity parameter a in the NCTVD and select the best combination of parameters to enhance the noise reduction performance of the constructed model, and parameter constraints are given to ensure the strict convexity of the model; then, the new NCTVD model is solved by the minimum optimization algorithm Finally, the Teager-Kaiser Energy Operator (TKEO) method is used to analyze the spectrum of the noise-reduced signal and to validate the application of motor bearing fault feature extraction. Experimental results of public and measured data show that the proposed method can not only effectively suppress noise interference and characterize fault information, but also improve the problems of pulse energy attenuation and poor sparse effect caused by traditional TVD model in the process of fault feature extraction.

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motor bearing / fault diagnosis / total variation denoising / beetle antennae search

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WANG Fan1,MA Jun1,2,WANG Xiaodong1,2,ZHU Jiangyan1. An improved TVD fault feature extraction method for motor bearing[J]. Journal of Vibration and Shock, 2023, 42(10): 203-214

References

[1] 杨国安. 旋转机械故障诊断实用技术[M]. 中国石化出版社，2012.
YANG Guo-an. Practical technique for fault diagnosis of rotating machinery[M]. China Petrochemical Press, 2012.
[2] 付嘉乐. 电机轴承故障诊断方法研究[D]. 兰州理工大学，2018.
FU Jia-le. Research on fault diagnosis method of motor bearing[D]. Lanzhou University of Technology, 2018.
[3] 王建国，李健，万旭东. 基于奇异值分解和局域均值分解的滚动轴承故障特征提取方法[J]. 机械工程学报，2015, 51(3): 104-110.
WANG Jian-guo, LI Jian, WAN Xu-dong. Fault Feature Extraction Method of Rolling Bearings Based on Singular Value Decomposition and Local Mean Decomposition[J]. Journal of Mechanical Engineering, 2015, 51(3): 104-110.
[4] 姜宏伟，袁朝辉，邱雷. 运用小波变换的飞机管路振动信号降噪方法[J]. 振动、测试与诊断，2012, 32(5): 827-830.
JIANG Hong-wei, YUAN Zhao-hui, QIU Lei. Wavelet Transform Based Denoising Method on Pipe Vibration Signal of Aircraft[J]. Journal of Vibration, Measurement and Diagnosis, 2012, 32(5): 827-830.
[5] 彭丹丹，刘志亮，靳亚强，等. 基于软筛分停止准则的改进经验模态分解及其在旋转机械故障诊断中的应用[J]. 机械工程学报，2019, 55(10): 122-132.
PENG Dan-dan, LIU Zhi-liang, JIN Ya-qiang,et al. Improved EMD with a Soft Sifting Stopping Criterion and Its Application to Fault Diagnosis of Rotating Machinery [J]. Journal of Mechanical Engineering, 2019, 55(10): 122-132.
[6] Rudin L I, Oshe R S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D Nonlinear Phenomena, 1992, 60: 259-268.
[7] Figueiredo M A T, Dias J B, Oliveira J P,et al. On total variation denoising: a new majorization-minimization algorithm and an experimental comparisonwith wavalet denoising[C]// IEEE International Conference on Image Processing. Atlanta: 2007.
[8] Laurent C. A directal gorithm for 1-D total variation denoising[J]. Signal Processing Letters, 2013, 20(11):1054-1057.
[9] 唐贵基，田甜，庞彬. 基于总变差去噪和快速谱相关的滚动轴承故障诊断[J]. 振动与冲击，2019, 38(11): 187-193.
TANG Gui-ji, TIAN Tian, PANG Bin. Rolling bearing fault diagnosis method based on total variation de-noising and fast spectral correlation[J]. Journal of Vibration and Shock, 2019, 38(11):187-193.
[10] 朱丹宸，张永祥，赵磊，等. 基于TVD和MSB的滚动轴承故障特征提取[J]. 振动与冲击，2019, 38(08): 103-109.
ZHU Dan-chen, ZHANG Yong-xiang, ZHAO Lei,et al. Fault feature extraction of rolling element bearings based on TVD and MSB[J]. Journal of Vibration and Shock, 2019, 38(08): 103-109.
[11] Yi C, Li Y, Dang Z,et al. A novel mechanical fault diagnosis scheme based on the convex 1-D second-order total variation denoising algorithm[J]. Applied Sciences, 2016, 6 (12) : 403.
[12] Jiang Yunlong, Chen Zhigang, YU Yue, et al. Fault diagnosis of rolling bearing based on TVD-VMD[C]// Proceedings of the 40th Chinese Control Conference. Shanghai: 2021.
[13] Nikolova M, Ng M K, Tam C P. Fast nonconvex nonsmooth minimization methods for image restoration and reconstruction[J]. IEEE Transactions on Image Processing, 2010, 19(12): 3073-3088.
[14] Selesnick I W, Parekh A, Bayram I. Convex 1-D total variation denoising with non-convex regularization[J]. IEEE Signal Processing Letters. 2015, 22(2): 141-144.
[15] Liu Y L, Du H, Wang Z, et al. Convex MR brain image reconstruction via non-convex total variation minimization[J]. International Journal of Imaging Systems and Technology, 2018, 28(4): 246-253.
[16] Tripathi P. Electroencephalogram signal quality enhancement by total variation denoising using non-convex regulariser[J]. Biomedical Engineering and Technology, 2020, 33(2): 134-145.
[17] Zhong D J, Yi C C, Xiao H. A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization[J]. Complexity, 2020, 20(10): 2813.
[18] Jiang X, Li S. BAS: Beetle Antennae Search Algorithm for Optimization Problems[J]. International Journal of Robotics and Control, 2017.
[19] Parekh A, Selesnick I W. Convex Fused Lasso Denoising with Non-Convex Regularization and its use for Pulse Detection[C]// Signal Processing in Medicine and Biology Symposium. Philadelphia: 2015.
[20] Zhang S, Wang Y, He S. and Jiang Z., Bearing fault di-agnosis based on variational mode decomposition and totalvariation denoising[J]. Measurement Science and Technology, 2016, 27(7): 1-10.
[21] Ou Y L, He S L, Hu C F,et al. Research on Rolling Bearing Fault Diagnosis Using Improved Majorization Minimization Based Total Variation and Empirical Wavelet Transform[J]. Shock and Vibration, 2020, 5(4): 1-11.