Mechanics & Industry
Volume 20, Number 1, 2019
|Number of page(s)||14|
|Published online||08 April 2019|
A method based on Dempster-Shafer theory and support vector regression-particle filter for remaining useful life prediction of crusher roller sleeve★
School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Gan Zhou 341000, PR China
2 Faculty of Foreign Studies, Jiangxi University of Science and Technology, Gan Zhou 341000, PR China
* e-mail: email@example.com
Accepted: 30 September 2018
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.
Key words: D-S theory / data fusion / RUL prediction / support vector regression / particle filter
© AFM, EDP Sciences 2019
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.