scholarly journals Prediction of the Remaining Useful Life of Lithium-ion Batteries Based on Dempster-Shafer Theory and the Support Vector Regression-particle Filter

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hancheng Dong
Keyword(s):  
Particle Filter ◽  
Support Vector Regression ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Dempster Shafer Theory ◽  
Useful Life ◽  
Shafer Theory

A method based on Dempster-Shafer theory and support vector regression-particle filter for remaining useful life prediction of crusher roller sleeve

2019 ◽  
Vol 20 (1) ◽  
pp. 106
Author(s):  
Haiping Liu ◽  
Jianjun Wu ◽  
Xiang Ye ◽  
Taijian Liao ◽  
Minlin Chen
Keyword(s):  
Particle Filter ◽  
Support Vector Regression ◽  
Prediction Method ◽  
Vibration Signal ◽  
Prediction Performance ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Dempster Shafer Theory ◽  
Useful Life ◽  
Shafer Theory

In order to solve the problem of accurately predicting the remaining useful life (RUL) of crusher roller sleeve under the partially observable and nonlinear nonstationary running state, a new method of RUL prediction based on Dempster-Shafer (D-S) data fusion and support vector regression-particle filter (SVR-PF) is proposed. First, it adopts the correlation analysis to select the features of temperature and vibration signal, and subsequently utilize wavelet to denoising the features. Lastly, comparing the prediction performance of the proposed method integrates temperature and vibration signal sources to predict the RUL with the prediction performance of single source and other prediction methods. The experiment results indicate that the proposed prediction method is capable of fusing different data sources to predict the RUL and the prediction accuracy of RUL can be improved when data are less available.

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