Long-range dependence and heavy tail characteristics for remaining useful life prediction in rolling bearing degradation

2022 ◽  
Vol 102 ◽  
pp. 268-284
Wanqing Song ◽  
He Liu ◽  
Enrico Zio
2017 ◽  
Vol 66 (4) ◽  
pp. 1368-1379 ◽  
Hanwen Zhang ◽  
Maoyin Chen ◽  
Xiaopeng Xi ◽  
Donghua Zhou

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Ying Zhang ◽  
Anchen Wang

The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of maintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics of rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features reextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is established using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a quantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input, the relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic remaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of rolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability distribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and engineering application value.

2020 ◽  
Vol 32 (2) ◽  
pp. 024006
Jian Tang ◽  
Guanhui Zheng ◽  
Dong He ◽  
Xiaoxi Ding ◽  
Wenbin Huang ◽  

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