Fault diagnosis of rolling bearings based on locally joint sparse marginal embedding

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (14) : 124-130.

ZHOU Hongdi，ZHANG Hang，ZHONG Fei

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Abstract

Rolling bearing is one of the important parts of machinery and equipment, and it is great significance to the safe operation of machinery and equipment through timely and effective monitoring and diagnosis. In order to address the high dimensionality and information redundancy caused by information fusion in existing bearing fault diagnosis methods, a novel bearing defection diagnosis method based on Locally Joint Sparse Marginal Embedding (LJSME) was proposed. LJSME uses L_2,1 -norm to reconstruct within-class and between-class matrices and introduces local graphs to preserve the neighborhood relations among high-dimensional features, and uses L_2,1 -norm as the regular term of the objective function to obtain the joint sparsity of feature extraction, thus ensuring the effectiveness of feature extraction. The method first extracts a high-dimensional feature dataset consisting of time-domain and frequency-domain information from the bearing vibration signal; then extracts sensitive low-dimensional features in the high-dimensional feature space dataset using LJSME; and finally achieves the fault pattern recognition of rolling bearings using a K-nearest neighbor classifier. The proposed method is validated by two sets of rolling bearing fault datasets, and the proposed algorithm can effectively extract sensitive features of rolling bearing vibration signals compared with other three dimensionality reduction algorithms.

Key words

fault diagnosis / local graph / feature extraction / locally joint sparse marginal embedding (LJSME) / rolling

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ZHOU Hongdi，ZHANG Hang，ZHONG Fei. Fault diagnosis of rolling bearings based on locally joint sparse marginal embedding[J]. Journal of Vibration and Shock, 2023, 42(14): 124-130

References

[1] 黄海松, 魏建安, 任竹鹏, 等. 基于失衡样本特性过采样算法与SVM的滚动轴承故障诊断[J]. 振动与冲击， 2020, 39(10): 65-74+132.
HUANG Hai-song, WEI Jian-an, REN Zhu-peng, et al. Rolling bearing fault diagnosis based on imbalanced sample characteristics oversampling algorithm and SVM [J]. Journal of vibration and shock, 2020, 39(10): 65-74+132.
[2] 周付明, 杨小强, 申金星, 等. 改进多元层次波动色散熵及其在滚动轴承故障诊断中的应用[J]. 振动与冲击， 2021, 40(22): 167-74.
ZHOU Fu-ming, YANG Xiao-qiang, SHEN Jin-xing,et al. Modified multivariate hierarchical fluctuation dispersion entropy and its application to the fault diagnosis of rolling bearings [J]. Journal of vibration and shock, 2021, 40(22): 167-74.
[3] 许子非, 金江涛, 李春. 基于多尺度卷积神经网络的滚动轴承故障诊断方法[J]. 振动与冲击，2021, 40(18): 212-20.
XU Zi-fei, JIN Jiang-tao, LI Chun. New method for the fault diagnosis of rolling bearings based on a multiscale convolutional neural network [J]. Journal of vibration and shock, 2021, 40(18): 212-20.
[4] YU J, HU T, LIU H. A New Morphological Filter for Fault Feature Extraction of Vibration Signals [J]. IEEE Access, 2019, 7: 53743-53.
[5] LI C, SANCHEZ R-V, ZURITA G, et al. Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis [J]. Neurocomputing, 2015, 168: 119-27.
[6] ZHANG H, HE Q. Tacholess bearing fault detection based on adaptive impulse extraction in the time domain under fluctuant speed [J]. Measurement Science and Technology, 2020, 31(7).
[7] LI H, FENG G, ZHEN D, et al. A Normalized Frequency-Domain Energy Operator for Broken Rotor Bar Fault Diagnosis [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-10.
[8] WANG D, TSUI K-L, QIN Y. Optimization of segmentation fragments in empirical wavelet transform and its applications to extracting industrial bearing fault features [J]. Measurement, 2019, 133: 328-40.
[9] YANG M, ZHANG L, YANG J, et al. Regularized robust coding for face recognition [J]. IEEE Trans Image Process, 2013, 22(5): 1753-66.
[10] WANG W, YUAN F, LIU Z. Sparsity discriminant preserving projection for machinery fault diagnosis [J]. Measurement, 2021, 173.
[11] GU Y-K, ZHOU X-Q, YU D-P, et al. Fault diagnosis method of rolling bearing using principal component analysis and support vector machine [J]. Journal of Mechanical Science and Technology, 2018, 32(11): 5079-88.
[12] MBOO C P, HAMEYER K. Fault Diagnosis of Bearing Damage by Means of the Linear Discriminant Analysis of Stator Current Features From the Frequency Selection [J]. IEEE Transactions on Industry Applications, 2016, 52(5): 3861-8.
[13] KANG S, MA D, WANG Y, et al. Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding [J]. Mechanical Systems and Signal Processing, 2017, 86: 40-57.
[14] DONG S, CHEN L, TANG B, et al. Rotating Machine Fault Diagnosis Based on Optimal Morphological Filter and Local Tangent Space Alignment [J]. Shock and Vibration, 2015, 2015: 1-9.
[15] PENG Y, LIU Y, CHENG J, et al. Remaining useful life prediction of rolling bearing using adaptive sparsest narrow-band decomposition and locality preserving projections [J]. Advances in Mechanical Engineering, 2019, 11(12).
[16] MIAO A, CHENG Z, LI P, et al. Locality‐preserving data modelling and its application in fault classification [J]. The Canadian Journal of Chemical Engineering, 2021.
[17] 徐斌, 曹娜, 王永利. 结合最近邻和拓展稀疏表示的SAR图像目标识别方法[J]. 南京理工大学学报， 2021, 45(05): 567-74.
XU Bin, CAO Na, WANG Yong-li. SAR image object recognition method combining nearest neighbor and extended sparse representation [J]. Journal of Nanjing University of Science and Technology, 2021, 45(05): 567-74.
[18] 余阿东. 基于深度字典学习的滚动轴承故障识别[J]. 机电工程， 2022, 39(02): 231-7.
YU A-dong.Fault recognition of rolling bearing with deep dictionary learning [J]. Electrical and Mechanical Engineering, 2022, 39(02): 231-7.
[19] QIAO L, CHEN S, TAN X. Sparsity preserving projections with applications to face recognition [J]. Pattern Recognition, 2010, 43(1): 331-41.
[20] QIAO L, CHEN S, TAN X. Sparsity preserving discriminant analysis for single training image face recognition [J]. Pattern Recognition Letters, 2010, 31(5): 422-9.
[21] CHEN G, LIU F, HUANG W. Sparse discriminant manifold projections for bearing fault diagnosis [J]. Journal of Sound and Vibration, 2017, 399: 330-44.
[22] YU W, ZHAO C. Sparse Exponential Discriminant Analysis and Its Application to Fault Diagnosis [J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5931-40.
[23] MO D, LAI Z, WONG W. Locally Joint Sparse Marginal Embedding for Feature Extraction [J]. IEEE Transactions on Multimedia, 2019, 21(12): 3038-52.
[24] ZHANG W, KANG P, FANG X, et al. Joint sparse representation and locality preserving projection for feature extraction [J]. International Journal of Machine Learning and Cybernetics, 2018, 10(7): 1731-45.
[25] WANG W, CHEN J, LI J, et al. Remote Targets Recognition Based on Adaptive Weighting Feature Dictionaries and Joint Sparse Representations [J]. Journal of the Indian Society of Remote Sensing, 2018, 46(11): 1863-70.
[26] LI H, JIANG T, ZHANG K. Efficient and robust feature extraction by maximum margin criterion [Z]. IEEE Trans Neural Netw. 2006: 157-65.10.1109/TNN.2005.860852
[27] WEN J, LAI Z, ZHAN Y, et al. The L2,1-norm-based unsupervised optimal feature selection with applications to action recognition [J]. Pattern Recognition, 2016, 60: 515-30.
[28] DENG X, TIAN X. Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection [J]. Chinese Journal of Chemical Engineering, 2013, 21(2): 163-70.
[29] LEI Y, HE Z, ZI Y, et al. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs [J]. Mechanical Systems and Signal Processing, 2007, 21(5): 2280-94.
[30] SUN J, HU G, WANG C, et al. Analog Circuit Soft Fault Diagnosis Based on Sparse Random Projections and K-Nearest Neighbor [J]. Scientific Programming, 2021, 2021: 1-9.
[31] LOPARO K A. Bearing vibration data set [Z]. 2003