scholarly journals Method for Determining Starting Point of Rolling Bearing Life Prediction Based on Linear Regression

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 923
Author(s):  
Gao ◽  
Lv ◽  
Wu ◽  
Si ◽  
Hu

Aimed at addressing the problem that the subjective selection of start prediction time (SPT) in rolling bearing remaining useful life (RUL) prediction will lead to excessive noise in the prediction signal, a linear-regression-based SPT point determination was proposed. The sliding window linear regression method was used to establish sliding windows in the root mean square (RMS) range to obtain the RMS gradient domain. The threshold for the RMS gradient was set, and the continuous trigger threshold mechanism to determine the SPT point was used. The experimental results show that the linear-regression-based method can adaptively determine the SPT point and improve the accuracy of life prediction.

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
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
Author(s):  
Jian Tang ◽  
Guanhui Zheng ◽  
Dong He ◽  
Xiaoxi Ding ◽  
Wenbin Huang ◽  
...  

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