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Reliability evaluation of rolling bearing based on improved threshold DT-CWT noise reduction and WPHM model
Published:2022-11-22 author:ZHAO Hong-mei, ZHOU Zhi-hong, YANG Sheng-bing. Browse: 1039 Check PDF documents
Reliability evaluation of rolling bearing based on improved threshold 
DT-CWT noise reduction and WPHM model


ZHAO Hong-mei1, ZHOU Zhi-hong2, YANG Sheng-bing3

(1.College of Mechanical Engineering, Tangshan Polytechnic College, Tangshan 063299, China;

2. College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108， China;

3.Fujian Yongan Bearing Co., Ltd., Sanming 366000, China)


Abstract:  Aiming at the difficulty of noise reduction of rolling bearing operation data set and the low prediction accuracy of its reliability evaluation model, a reliability evaluation method of rolling bearing based on improved threshold DT-CWT noise reduction and WPHM model was proposed. Firstly, in data noise reduction, the dual-tree continuous wavelet transform (DT-CWT) was used to discretize the scale factor and translation factor, and the signal was completely reconstructed by two sets of parallel and independent low-pass and high-pass filters to form a real part tree and an imaginary part tree. Secondly, in terms of data processing, the global optimal search strategy of particle swarm optimization (PSO) was fused with least squares (LS) to quickly find the optimal parameters of Weibull proportional hazards model (WPHM) using the full-life test data of PRONOSTIA experimental bench. Finally, using the data of PRONOSTIA bearing test-bed of FEMTO, FRANCESCONTE University, France, the noise reduction effect of the improved DT-CWT threshold was evaluated, and the reliability evaluation curve calculated by WPHM model was verified. The results indicate that, compared with the traditional threshold function, the improved threshold DT-CWT function has better noise reduction effect, the signal-to-noise ratio (SNR) can be improved to 58.2% and the root mean square error (RMSE) can be reduced to 58.3% by the DT-CWT, and the trend of the noise reduction signal remains the same as the original signal. Parameter estimation of β, η and γ using full-life experimental data and PSO-LS method, solving the parameter estimation problem in WPHM model. The reliability evaluation curve is consistent with the actual degradation state of the bearing, and the WPHM model can reflect the health state of the bearing.

Key words:  bearing performance degradation; dualtree continuous wavelet transform (DT-CWT); particle swarm optimization (PSO); least squares (LS) algorithm; Weibull proportional hazards model (WPHM); data noise reduction algorithm; optimal parameters

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