| Citation: | WEN Jinpeng, LI Junning, LUO Wenguang, et al. Fault diagnosis of the Hybrid ceramic bearing in whole life cycle based on HO-LSSVM[J]. Journal of Aerospace Power, 2026, 41（6）：20240545 doi: 10.13224/j.cnki.jasp.20240545
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In order to solve the problem of low accuracy of the Least squares support vector machine (LSSVM) classification model in the whole life cycle fault diagnosis of rolling bearings, a whole life cycle fault diagnosis method of hybrid ceramic bearings was proposed based on wavelet threshold denoising, beluga whale optimization-variational mode decomposition (BWO-VMD), and hippopotamus optimization algorithm-least squares support vector machine (HO-LSSVM). The wavelet threshold denoising method was adopted to denoise the vibration signal. The Beluga Whale Optimization algorithm was used to optimize the parameters of the Variational Mode Decomposition. The Hippo Optimization algorithm was used to optimize the parameters of the least squares support vector machine to construct the fault diagnosis model. Based on the whole life cycle vibration data collected by the high-performance rolling bearing comprehensive performance experimental bench of the research group, the root mean square value and peak-to-peak value were used as the basis for the division of the whole life cycle fault stage, and the whole life cycle fault state identification of the hybrid ceramic bearing was realized. The results showed that the recognition accuracy of the proposed method was higher than that of traditional methods such as the Convolutional Neural Network (CNN), up to 99.38%.
| [1] |
KANKAR P K, SHARMA S C, HARSHA S P. Rolling element bearing fault diagnosis using wavelet transform[J]. Neurocomputing, 2011, 74(10): 1638-1645. doi: 10.1016/j.neucom.2011.01.021
|
| [2] |
MBA D. The use of acoustic emission for estimation of bearing defect size[J]. Journal of Failure Analysis and Prevention, 2008, 8(2): 188-192. doi: 10.1007/s11668-008-9119-8
|
| [3] |
RANDALL R B, ANTONI J. Rolling element bearing diagnostics: a tutorial[J]. Mechanical Systems and Signal Processing, 2011, 25(2): 485-520. doi: 10.1016/j.ymssp.2010.07.017
|
| [4] |
FENG Zhigang, WANG Shouqi, YU Mingyue. A fault diagnosis for rolling bearing based on multilevel denoising method and improved deep residual network[J]. Digital Signal Processing, 2023, 140: 104106. doi: 10.1016/j.dsp.2023.104106
|
| [5] |
BRUSA E, DELPRETE C, GARGIULI S, et al. Screening of discrete wavelet transform parameters for the denoising of rolling bearing signals in presence of localised defects[J]. Sensors, 2023, 23(1): 8. doi: 10.3390/s23010008
|
| [6] |
LU Wenqing, ZHANG Laibin, LIANG Wei, et al. Research on a small-noise reduction method based on EMD and its application in pipeline leakage detection[J]. Journal of Loss Prevention in the Process Industries, 2016, 41: 282-293. doi: 10.1016/j.jlp.2016.02.017
|
| [7] |
GUO Hualing, BIN Z, LIU Liping, et al. Study on denoising method of surface defect signal of rail based on CEEMD and wavelet soft threshold[J]. Acoustical Physics, 2023, 69(6): 929-935. doi: 10.1134/S1063771022600504
|
| [8] |
WANG Lihua, ZHAO Xiaoping, WU Jiaxin, et al. Motor fault diagnosis based on short-time Fourier transform and convolutional neural network[J]. Chinese Journal of Mechanical Engineering, 2017, 30(6): 1357-1368. doi: 10.1007/s10033-017-0190-5
|
| [9] |
QI Bowei, LI Yuanyuan, YAO Wei, et al. Application of EMD combined with deep learning and knowledge graph in bearing fault[J]. Journal of Signal Processing Systems, 2023, 95(8): 935-954. doi: 10.1007/s11265-023-01845-z
|
| [10] |
DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544. doi: 10.1109/TSP.2013.2288675
|
| [11] |
LI Junning, CHEN Wuge, HAN Ka, et al. Fault diagnosis of rolling bearing based on GA-VMD and improved WOA-LSSVM[J]. IEEE Access, 2020, 8: 166753-166767. doi: 10.1109/ACCESS.2020.3023306
|
| [12] |
ZHU Xingtong, XIONG Jianbin, LIANG Qiong. Fault diagnosis of rotation machinery based on support vector machine optimized by quantum genetic algorithm[J]. IEEE Access, 2018, 6: 33583-33588. doi: 10.1109/ACCESS.2018.2789933
|
| [13] |
ZHOU Junbo, XIAO Maohua, NIU Yue, et al. Rolling bearing fault diagnosis based on WGWOA-VMD-SVM[J]. Sensors, 2022, 22(16): 6281. doi: 10.3390/s22166281
|
| [14] |
DONOHO D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627. doi: 10.1109/18.382009
|
| [15] |
LI Junning, LUO Wenguang, BAI Mengsha, et al. Fault diagnosis of high-speed rolling bearing in the whole life cycle based on improved grey wolf optimizer-least squares support vector machines[J]. Digital Signal Processing, 2024, 145: 104345. doi: 10.1016/j.dsp.2023.104345
|
| [16] |
LU Jingyi, LIN Hong, YE Dong, et al. A new wavelet threshold function and denoising application[J]. Mathematical Problems in Engineering, 2016, 2016(1): 3195492.
|
| [17] |
ZHONG Changting, LI Gang, MENG Zeng. Beluga whale optimization: a novel nature-inspired metaheuristic algorithm[J]. Knowledge-Based Systems, 2022, 251: 109215. doi: 10.1016/j.knosys.2022.109215
|
| [18] |
HEIDARI A A, PAHLAVANI P. An efficient modified grey wolf optimizer with Lévy flight for optimization tasks[J]. Applied Soft Computing, 2017, 60: 115-134. doi: 10.1016/j.asoc.2017.06.044
|
| [19] |
LI Hua, LIU Tao, WU Xing, et al. An optimized VMD method and its applications in bearing fault diagnosis[J]. Measurement, 2020, 166: 108185. doi: 10.1016/j.measurement.2020.108185
|
| [20] |
BANDT C, POMPE B. Permutation entropy: a natural complexity measure for time series[J]. Physical Review Letters, 2002, 88(17): 174102. doi: 10.1103/PhysRevLett.88.174102
|
| [21] |
YANG Yu, YUDEJIE, CHENG Junsheng. A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J]. Journal of Sound and Vibration, 2006, 294(1/2): 269-277. doi: 10.1016/j.jsv.2005.11.002
|
| [22] |
AMIRI M H, MEHRABI HASHJIN N, MONTAZERI M, et al. Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm[J]. Scientific Reports, 2024, 14: 5032. doi: 10.1038/s41598-024-54910-3
|
| [23] |
SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300. doi: 10.1023/A:1018628609742
|
| [24] |
JIN Zhihao, CHEN Guangdong, YANG Zhengxin. Rolling bearing fault diagnosis based on WOA-VMD-MPE and MPSO-LSSVM[J]. Entropy, 2022, 24(7): 927. doi: 10.3390/e24070927
|
| [25] |
SMITH W A, RANDALL R B. Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64/65: 100-131.
|
| [26] |
LV Mingzhu, LIU Shixun, SU Xiaoming, et al. General log-linear weibull model combining vibration and temperature characteristics for remaining useful life prediction of rolling element bearings[J]. Shock and Vibration, 2020, 2020: 8829823. doi: 10.1155/2020/8829823
|
| [27] |
SHEN Weijie, XIAO Maohua, WANG Zhenyu, et al. Rolling bearing fault diagnosis based on support vector machine optimized by improved grey wolf algorithm[J]. Sensors, 2023, 23(14): 6645. doi: 10.3390/s23146645
|
| [28] |
ZHANG Lipin, LU Chen, TAO Laifa. Curve similarity recognition based rolling bearing degradation state estimation and lifetime prediction[J]. Journal of Vibroengineering, 2016, 18(5): 2839-2854. doi: 10.21595/jve.2016.17377
|
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