An end-to-end framework for remaining useful life prediction of rolling bearing based on feature pre-extraction mechanism and deep adaptive transformer model

2021 ◽  
pp. 107531
Author(s):  
Xuanyuan Su ◽  
Hongmei Liu ◽  
Laifa Tao ◽  
Chen Lv ◽  
Mingliang Suo
Keyword(s):  
Life Prediction ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Extraction Mechanism ◽  
Useful Life ◽  
End To End ◽  
Transformer Model

Remaining Useful Life Prediction Using a Novel Feature-Attention-Based End-to-End Approach

2021 ◽  
Vol 17 (2) ◽  
pp. 1197-1207
Author(s):  
Hui Liu ◽  
Zhenyu Liu ◽  
Weiqiang Jia ◽  
Xianke Lin
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life ◽  
Feature Attention ◽  
End To End

scholarly journals Remaining Useful Life Prediction of Rolling Bearings Using Electrostatic Monitoring Based on Two-Stage Information Fusion Stochastic Filtering

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ying Zhang ◽  
Anchen Wang
Keyword(s):  
Life Prediction ◽  
Engineering Application ◽  
Rolling Bearing ◽  
Gaussian Mixture ◽  
Remaining Useful Life ◽  
Joint Probability Distribution ◽  
Rolling Bearings ◽  
Two Stage ◽  
Stochastic Filtering ◽  
Useful Life

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.

Rolling bearing remaining useful life prediction via weight tracking relevance vector machine

2020 ◽  
Vol 32 (2) ◽  
pp. 024006
Author(s):  
Jian Tang ◽  
Guanhui Zheng ◽  
Dong He ◽  
Xiaoxi Ding ◽  
Wenbin Huang ◽  
...  
Keyword(s):  
Life Prediction ◽  
Rolling Bearing ◽  
Relevance Vector Machine ◽  
Remaining Useful Life ◽  
Useful Life

A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing

Measurement ◽  
2019 ◽  
Vol 135 ◽  
pp. 678-684 ◽  
Author(s):  
Lingli Cui ◽  
Xin Wang ◽  
Yonggang Xu ◽  
Hong Jiang ◽  
Jianping Zhou
Keyword(s):  
Kalman Filter ◽  
Life Prediction ◽  
Unscented Kalman Filter ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Filter Method ◽  
Useful Life ◽  
Kalman Filter Method
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