Fault diagnosis method of rotating machinery based on global-local Euler elastic discriminant projection
SU Shuzhi1,2, ZHANG Maoyan1, FANG Xianjin1,2, ZHU Yanmin3
1.School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China;
2.Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230031, China;
3.School of Computer Mechanical Engineering, Anhui University of Science & Technology, Huainan 232001, China
Abstract：Fault diagnosis methods are usually sensitive to outliers, and it is difficult to extract global and local discriminant information at the same time, resulting in poor separation between low-dimensional discriminant feature subsets. To solve this problem, a fault diagnosis method of rotating machinery was proposed based on Global-Local Euler Elastic Discriminant Projection (GLEEDP). This method maps the high-dimensional fault features to the Euler representation space through the cosine metrics, and expands the differences between heterogeneous fault samples. Then, an optimization model based on three objective functions of global, local and within-class scatter is constructed in this space, which further improves the local intra class aggregation and global inter class separation of low dimensional discriminant feature subsets on the basis of maintaining the overall structure. The experimental results on two mechanical fault datasets of bearing and gearbox show that the proposed method can effectively explore the fault discrimination information and has superior fault diagnosis performance.
[1] Liu Y, Hu Z, Zhang Y. Bearing feature extraction using multi-structure locally linear embedding[J]. Neurocomputing, 2021, 428(10): 280-290.
[2] Shi M, Zhao R, Wu Y, et al. Fault diagnosis of rotor based on Local-Global Balanced Orthogonal Discriminant Projection[J]. Measurement, 2021, 168(2): 108320.
[3] 常书源, 赵荣珍, 陈博, 等. 基于边界判别多流形分析的故障数据集降维方法[J].振动与冲击, 2021，40(23):120-126.
CHANG Shuyuan, ZHAO Rongzhen, CHEN Bo, et al. Dimension reduction method of fault data set based on BDMA. Journal of Vibration and Shock, 2021, 40(23): 120-126.
[4] 戚晓利, 王振亚, 吴保林，等. 基于VMD-ICMSE和半监督判别SOINN L-Isomap的滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(4): 252-260.
QI Xiaoli, WANG Zhenya, WU Baolin, et al. A rolling bearing fault diagnosis method based on VMD-ICMSE and semi-supervised discriminant SOINN L-Isomap. Journal of Vibration and Shock, 2020, 39(4): 252-260.
[5] Wang W, Yuan F, Liu Z. Sparsity discriminant preserving projection for machinery fault diagnosis[J]. Measurement, 2021, 173(7): 108488.
[6] Deng L F, Zhang A H, Zhao R Z. Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM[J]. Advances in Mechanical Engineering, 2022, 14(1):1–18.
[7] Wang H, Huang H, Yu S, et al. Size and Location Diagnosis of Rolling Bearing Faults: An Approach of Kernel Principal Component Analysis and Deep Belief Network[J]. International Journal of Computational Intelligence Systems, 2021, 14(1): 1672-1686.
[8] Zhong K, Han M, Qiu T, et al. Fault diagnosis of complex processes using sparse kernel local Fisher discriminant analysis[J]. IEEE transactions on neural networks and learning systems, 2019, 31(5): 1581-1591.
[9] 梁超, 路鹏, 郜宁,等. 基于LPP的转子振动故障特征提取方法[J]. 振动工程学报, 2018, 31(3): 539-544.
LIANG Chao, LU Peng, GAO Ning, et al. Feature Extraction of Rotor Vibration Fault Based on LPP Algorithm[J]. Journal of Vibration Engineering, 2018, 1(3): 539-544.
[10] Zhang Y, Ye D, Liu Y. Robust locally linear embedding algorithm for machinery fault diagnosis[J]. Neurocomputing, 2018, 273(3): 323-332.
[11] Zhao X, Jia M. Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis[J]. Neurocomputing, 2018, 315(45): 447-464.
[12] 马萍, 张宏立, 范文慧. 基于局部与全局结构保持算法的滚动轴承故障诊断 [J]. 机械工程学报, 2017, 53(2): 20-25.
MA Ping, ZHANG Hongli，FAN Wenhui. Fault Diagnosis of Rolling Bearings Based on Local and Global Preserving Embedding Algorithm[J]. Journal of Mechanical Engineering, 2017, 53(2): 20-25.
[13] 王望望, 邓林峰, 赵荣珍,等. 集成KPCA与t‑SNE的滚动轴承故障特征提取方法[J]. 振动工程学报, 2021,34(2): 431-440.
WANG Wangwang，DENG Linfeng，ZHAO Rongzhen, et al. Fault feature extraction of rolling bearing integrating KPCA and t-SNE[J]. Journal of Vibration Engineering, 2021, 34(2): 431-440.
[14] Rahimi S, Aghagolzadeh A, Ezoji M. Human action recognition based on the Grassmann multi-graph embedding[J]. Signal Image and Video Processing, 2019, 13(3): 271-279
[15] Liu Y, Gao Q, Han J, et al. Euler sparse representation for image classification[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1): 3691-3697.
[16] Mekonen B D, Salilew G A. Geometric Series on Fourier Cosine-Sine Transform[J]. Journal of Advances in Mathematics and Computer Science.2018, 28(4): 1-4.
[17] Fitch A, Kadyrov A, Christmas W, et al. Fast robust correlation[J]. IEEE Transactions on Image Processing, 2005，70(7):1063–1073.
[18] Peng L, Yao Q W, Least absolute deviations estimation for ARCH and GARCH models[J], Biometrika, 2003, 90(4): 967–975.
[19] 李辉，郝如江. 基于信息融合和广义循环互相关熵的电机轴承故障诊断[J]. 振动与冲击, 2022, 41(2): 200-207.
LI Hui, HAO Rujiang. Rolling bearing fault diagnosis based on sensor information fusion and generalized cyclic cross correntropy spectrum density. Journal of Vibration and Shock, 2022, 41(2): 200-207.
[20] Li X, Yang Y, Shao H, et al. Symplectic weighted sparse support matrix machine for gear fault diagnosis[J]. Measurement, 2021, 168(2): 108392.
[21] Luo T, Hou C, Yi D, et al. Discriminative orthogonal elastic preserving projections for classification[J]. Neurocomputing, 2016, 179(9): 54-68.
[22] Long T H, Sun Y F, Gao J B, et al. Locality preserving projection based on Euler representation[J]. Journal of Visual Communication and Image Representation, 2020, 70(5): 102796.
