Research on the prediction of bearing life based on EMD and PF

Weilin Li; Feng Chen; Hangchao Zhou; Yinda He

doi:10.1117/12.2652707

2 November 2022 Research on the prediction of bearing life based on EMD and PF

Weilin Li, Feng Chen, Hangchao Zhou, Yinda He

Proceedings Volume 12351, International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022); 123511O (2022) https://doi.org/10.1117/12.2652707
Event: International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), 2022, Nanjing, China

Abstract

Bearings are widely used in industrial production lines and rotating machinery equipment, and bearing failure may prevent the operation of the entire system, so its reliability is very important. It is particularly important to repair and maintain bearings before failure, so we need to accurately know the residual life of bearings and assess the degradation state of bearings. Therefore, this paper puts forward a bearing life prediction method based on EMD(empirical mode decomposition) and PF (particle filter). First, aiming at the non-linear and non-stationary features of vibration signals of bearings, EMD is conducted for signals, which mainly eliminates the noise mixed in the original signals effectively through effective component reconstruction, analyzes the intrinsic mode component obtained and extract its energy entropy, and take the energy entropy feature as the degradation characteristics indicating the heath state of bearings. Second, the performance degradation model of bearings is built according to the trend characteristics, and PFis used to update the parameters of the performance degradation model of bearings for predicting the remaining life. In order to verify its effectiveness, the method proposed is verified with two groups of whole-life data of bearings and compared with the traditional ARIMA model. The experimental results show that the RMSE value calculated by the proposed EMD-PF prediction model is significantly smaller than that of the ARIMA model, and the prediction error decreases gradually with the degradation process. The prediction model proposed has stable convergence and can effectively predict the degradation process of bearings.

Citation Download Citation

Weilin Li, Feng Chen, Hangchao Zhou, and Yinda He "Research on the prediction of bearing life based on EMD and PF", Proc. SPIE 12351, International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), 123511O (2 November 2022); https://doi.org/10.1117/12.2652707

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KEYWORDS

Data modeling

Particles

Particle filters

Failure analysis

Statistical modeling

Complex systems

Instrument modeling

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