TRPCA rolling bearing fault feature extraction method with joint constraints of L 1,1,2 norm and tensor kernel norm under variable rotating speed

WANG Ran1, CAO Xu1, ZHANG Junwu1, YU Liang2

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (7) : 84-93.

PDF(4256 KB)
PDF(4256 KB)
Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (7) : 84-93.

TRPCA rolling bearing fault feature extraction method with joint constraints of L 1,1,2 norm and tensor kernel norm under variable rotating speed

  • WANG Ran1, CAO Xu1, ZHANG Junwu1, YU Liang2
Author information +
History +

Abstract

As one of the important components of rotating mechanical equipment, the effective extraction of fault features from rolling bearings is of great importance to ensure the regular operation of mechanical equipment. In practical applications, rolling bearings usually operate at variable speeds, and the non-stationary signal of the bearings collected by a single sensor are often covered by severe background noise, making the task of fault feature extraction very difficult. This paper proposes a robust fault feature extraction method based on time-frequency analysis under variable speed conditions. First, the time-frequency representation (TFR) is used as frontal slices to construct the tensor, and explore the tubewise sparsity of time-varying fault features and the low rank property of the background noise in the tensor domain. Then, the tensor robust principal component analysis (TRPCA) with joint constraints of norm and tensor nuclear norm is used to extract the fault feature tensor, obtaining a tubewise sparse fault feature tensor. Finally, the extracted fault feature tensor is fused the channel index to get a time-frequency representation that can effectively represent the fault features. Simulation and experimental analyses verify the effectiveness of this method in bearing fault feature extraction.

Key words

tensor / fault feature extraction / variable speed conditions / TRPCA / tubewise sparsity

Cite this article

Download Citations
WANG Ran1, CAO Xu1, ZHANG Junwu1, YU Liang2. TRPCA rolling bearing fault feature extraction method with joint constraints of L 1,1,2 norm and tensor kernel norm under variable rotating speed[J]. Journal of Vibration and Shock, 2024, 43(7): 84-93

References

[1] 陈鑫, 郭瑜, 伍星, 等. 基于CPW和SCD的行星轴承内圈故障特征提取 [J]. 振动测试与诊断, 2021, 41(05): 868-73+1030. CHEN Xin, GUO Yu , WU Xing, et al. Fault Feature Extraction of Planetary Bearing Inner Ring Based on CPW and SCD [J]. Journal of Vibration, Measurement & Diagnosis. 2021, 41(05): 868-73+1030. [2] 王冉, 周雁翔, 胡雄, 等. 基于EMD多尺度威布尔分布与HMM的轴承性能退化评估方法 [J]. 振动与冲击, 2022, 41(03): 209-15. WANG Ran, ZHOU Yanxiang, HU Xiong, et al. Evaluation method of bearing performance degradation based on EMD multi-scale Weibull distribution and HMM. [J]. Journal of Vibration and Shock.2022. 41(3): 209-215. [3] BORGHESANI P, SMITH W A, RANDALL R B, et al. Bearing signal models and their effect on bearing diagnostics [J]. Mechanical Systems and Signal Processing, 2022, 174: 109077. [4] 张旭辉, 张超, 樊红卫, 等. 快速谱峭度结合阶次分析滚动轴承故障诊断 [J]. 振动测试与诊断, 2021, 41(06): 1090-5+235. ZHANG Xuhui, ZHANG Chao, FAN Hongwei, et al. Improved Fault Diagnosis of Rolling Bearing by Fast Kurtogram and Order Analysis [J]. Journal of Vibration, Measurement & Diagnosis. 2021, 41(06): 1090-5+235. [5] 张龙, 吴荣真, 周建民, 等. 滚动轴承性能退化的时序多元状态估计方法 [J]. 振动测试与诊断, 2021, 41(06): 1096-104+235-236. ZHANG Long,WU Rongzhen,ZHOU Jianmin, et al. Performance Degradation Assessment of Rolling Bearing Based on AR Model and Multivariate State Estimation Technique [J]. Journal of Vibration, Measurement & Diagnosis. 2021, 41(06): 1096-104+235-236. [6] WANG R, ZHANG J, FANG H, et al. Sparsity enforced time–frequency decomposition in the Bayesian framework for bearing fault feature extraction under time-varying conditions [J]. Mechanical Systems and Signal Processing, 2023, 185: 109755. [7] YU L, ANTONI J, LECLèRE Q. COMBINED REGULARIZATION OPTIMIZATION FOR SEPARATING TRANSIENT SIGNAL FROM STRONG NOISE IN ROLLING ELEMENT BEARING DIAGNOSTICS; proceedings of the Proceedings of Surveillance 7, F, 2013 [C]. [8] 刘伟, 刘洋, 单雪垠, 等. 基于低秩-稀疏分解的滚动轴承故障特征提取 [J]. 北京化工大学学报(自然科学版), 2022, 49(06): 83-91. LIU Wei, LIU Yang, SHAN XueYin, et al .Feature extraction for fault signals from a rolling bearing using low rank and sparse decomposition [J]. Journal of Beijing University of Chemical Technology, 2022, 49(6): 83-91. [9] KOLDA T. Tensor decompositions and applications [J]. Siam Review, 2009. [10] WANG A, JIN Z, YANG J. A Factorization Strategy for Tensor Robust PCA; proceedings of the Pattern Recognition, Cham, F 2020//, 2020 [C]. Springer International Publishing. [11] LU C, FENG J, CHEN Y, et al. Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(4): 925-38. [12] ZHANG Z, ELY G, AERON S, et al. Novel methods for multilinear data completion and de-noising based on tensor-SVD; proceedings of the IEEE, F, 2014 [C]. [13] GE M, LV Y, MA Y. Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis [J]. IEEE Access, 2020, 8: 178425-49. [14] 胡超凡, 王衍学. 基于张量分解的滚动轴承复合故障多通道信号降噪方法研究 [J]. 机械工程学报, 2019, 55(12): 50-7. HU Chaofan, WANG Yanxue. Research on Multi-channel Signal Denoising Method for Multiple Faults Diagnosis of Rolling Element Bearings Based on Tensor Factorization [J]. Journal of Mechanical Engineering, 2019, 55(12): 50-57. [15] XU Y, WU Z, CHANUSSOT J, et al. Joint Reconstruction and Anomaly Detection From Compressive Hyperspectral Images Using Mahalanobis Distance-Regularized Tensor RPCA [J]. IEEE Transactions on Geoence and Remote Sensing, 2018: 2919-30. [16] HOU F, SELESNICK I, CHEN J, et al. Fault diagnosis for rolling bearings under unknown time-varying speed conditions with sparse representation [J]. Journal of Sound and Vibration, 2021, 494: 115854.
PDF(4256 KB)

639

Accesses

0

Citation

Detail

Sections
Recommended

/