Acoustic Emission Signal Diagnosis of Rolling Bearing Faults Based on CEEMDAN and MESSA-SVM

Zhang Xu; Yu Yang

doi:10.54097/fjhj7589

Authors

Zhang Xu
Yu Yang

DOI:

https://doi.org/10.54097/fjhj7589

Keywords:

Bearing Fault Diagnosis, Acoustic Emission, CEEMDAN, Permutation Entropy, Sparrow Search Algorithm, Support Vector Machin

Abstract

Acoustic emission signals generated during rolling bearing failures reflect their health conditions, serving as crucial indicators for to address the limitations in feature extraction and classification accuracy of traditional methods in acoustic emission signal to address the limitations in feature extraction and classification accuracy of traditional methods in acoustic emission signal, this paper proposes a fault diagnosis method based on CEEMDAN and MESSA-SVM. Initially, CEEMDAN is employed to perform multi-scale Initially, CEEMDAN is employed to perform multi-scale decomposition of fault acoustic emission signals, obtaining Intrinsic Mode Function (IMF) components, followed by selecting critical IMF components The IMF components are selected through kurtosis-correlation feature screening. Permutation entropy is then calculated to construct feature vectors. To optimize SVM classifier parameters, a Multi-strategy Sparrow Search Algorithm (MESSA) incorporating Euclidean distance population diversity, Gaussian perturbation strategy, and fitness difference guidance is designed. This algorithm demonstrates excellent global optimization capability in standard test functions. This algorithm demonstrates excellent global optimization capability in standard test functions. Experimental results indicate that the MESSA-SVM classifier achieves 95% accuracy. Through comparative analysis with other algorithms, the advantages of this method over alternatives include the advantages of this method over alternatives are validated, demonstrating its effectiveness and reliability in bearing fault acoustic emission signal diagnosis.

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References

[1] YU Yang, HE Ming, LIU Bo, et al. Recognition of rolling bearing fault acoustic emission signal based on GA-IDBN[J]. Nondestructive Testing, 2020, 42(01):31-36.

[2] HUANG Hongchen, HAN Zhennan, ZHANG Qianqian, et al. Rolling bearing fault identification based on Laplace feature mapping[J]. Vibration and Shock 2015, 34(5):128-134+144.

[3] XIE X P, CHENG S J, LI X Y. Vibration diagnosis and optimization of industrial robot based on TPA and EMD methods [J]. Computer Modeling in Engineering & Sciences, 2023, 135( 3)

[4] VAN H B, HE D, QU Y. On the use of spectral averaging of acoustic emission signals for bearing fault diagnostics[J]. 2014, 136 (6): 1 - 13.

[5] M. E. Torres, M. A. Colominas, et al. "A complete ensemble empirical mode decomposition with adaptive noise," 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 2011, pp. 4144-4147.

[6] Xiang Mingsheng, Feng Kun, Jia Shaohui, et al. Multi-fault diagnosis method of centrifugal pump based on EMD-AE preferred features under strong noise conditions[J]. Vibration and Shock, 2024, 43(23):66-74.

[7] WEI Wei, WANG Zhihai, LIU Xiaoqin, et al. Acoustic emission diagnosis of rolling bearing faults based on MDFF and ISSA[J]. Vibration and Shock, 2023, 42(07):65-76.

[8] HE Cheng, WU Tao, GU Runwei, et al. Rolling bearing fault diagnosis based on composite multiscale permutation entropy and reverse cognitive fruit fly optimization algorithm - Extreme learning machine[J]. Measurement, 2021, 173: 108636.

[9] YU Yang, LI Eddie, YANG Ping, et al. Feature extraction of rolling bearing fault acoustic emission signal with improved wavelet threshold function and ACEWT method[J]. Vibration and Shock, 2023, 42(17): 194-202.

[10] Lv Fengxia, Miao Yi, Bie Fengfeng, et al. Application of ICEEMDAN and GS-SVM algorithms in rolling bearing acoustic emission fault diagnosis[J]. Noise and Vibration Control, 2022, 42(06):92-97+129.

[11] Dang J., Luo Y., Tian Lulin, et al. Optimization-based VMD fusion of information entropy and FA-PNN for wind turbine gearbox fault diagnosis[J]. Journal of Solar Energy, 2021, 42(01):198-204.

[12] DING Jiaxin, WANG Zhenya, YAO Lizang, et al. Generalized composite multiscale weighted arrangement entropy and parameter optimized support vector machine for rolling bearing fault diagnosis[J]. China Mechanical Engineering, 2021, 32(02):147-155.

[13] Wang Y, Zheng HZ, Huang X, et al. Sparrow search optimization algorithm based on multi-stage scheduling framework[J]. Journal of Electronics, 2024, 52(09):3086-3096.

[14] YANG X, ZHANG T, LI YM, et al. ISSA optimized SVM for rolling bearing fault diagnosis of electric motors[J]. Electronic Measurement Technology, 2023, 46(15):186-192.

[15] LI Xinyan, JIN Wuyin. Research on rolling bearing fault diagnosis based on optimized support vector machine with improved sparrow algorithm[J]. Vibration and Shock, 2023, 42(06): 106-114.

[16] HU Xuan, LI Chun, YE Kehua, et al. Application of improved gray wolf algorithm optimized support vector machine in wind turbine gearbox fault diagnosis[J]. Mechanical Strength, 2021, 43(06):1289-1296.

[17] YUAN Xianfeng, YAN Zichen, ZHOU Fengyu, et al. Rolling bearing fault diagnosis by SSAE and IGWO-SVM[J]. Vibration. Test and Diagnosis, 2020, 40(02):405-413+424.

[18] Li R, Fan YG. Fault diagnosis of high-pressure diaphragm pump check valve based on CEEMDAN multi-scale arrangement entropy and SO-RELM[J]. Vibration and Shock, 2023, 42(05):127-135.