Fuzzy Information Granulation and Improved RVM for Rolling Bearing Life Prediction

doi:10.16182/j.issn1004731x.joss.21-FZ0703

Abstract

Abstract: Aiming at the low accuracy in life prediction and unpredictable problems of degenerative performance trends and fluctuation ranges, etc. Of the bearing life prediction, an improved complete ensemble empirical mode decomposition with adaptive noise analysis and fuzzy information granulating method of improved relevance vector machine is proposed. Focusing on bearing data containing a lot of noise, through the improved complete ensemble empirical mode decomposition with adaptive noise analysis in combination with wavelet packet denoising, the principal component analysis is carride out by exitracing a variety of characeteristics of the signal, the effective information is extracted by granulating the fuzzy information, by entering the improved particle swarm algorithm to optimize the relevance vector machine model of the degradation index range and remaining life is predicted. The results show that the method can effectively predict the fluctuation range, and the residual life prediction accuracy is improved greatly.

Key words: improved complete ensemble empirical mode decomposition with adaptive noise, principal component analysis, bearing remaining life, fuzzy information granulation, particle swarm algorithm, relevance vector machine

CLC Number:

TP391.9

Hu Xiaoman, Wang Yan, Ji Zhicheng. Fuzzy Information Granulation and Improved RVM for Rolling Bearing Life Prediction[J]. Journal of System Simulation, 2021, 33(11): 2561-2571.

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