A novel method based on least squares support vector regression combing with strong tracking particle filter for machinery condition prognosis. (April 2014)

• Record Type: Journal Article
• Authors: Li, Chengliang; Wang, Zhongsheng; Bu, Shuhui; Jiang, Hongkai; Liu, Zhenbao
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 228:Number 6(2014:Jun.)
• Page Start: 1048
• Page End: 1062
• Publication Date: 2014-04
• Subjects: Least squares support vector regression -- strong tracking particle filter -- condition prognosis -- saltatory states; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/0954406213494158
• Languages: English
• ISSNs: 0954-4062

Abstract

A reliable prediction method is very important to avoid a catastrophic failure. This paper presents a novel method for machinery condition prognosis, named least squares support vector regression strong tracking particle filter which is based on least squares support vector regression combing with strong tracking particle filter. There are two main contributions in our work: first, the regression function of least squares support vector regression is extended, which constructs a bridge for the application of combining data-driven method with a recursive filter based on extend Kalman filter; second, an extend Kalman filter-based particle filter is studied by introducing a strong tracking filter into a particle filter. The strong tracking filter is used to update particles and produce importance densities which can improve the performance of the particle filter in tracking saltatory states, and finally strong tracking particle filter improves the prediction performance of least squares support vector regression in predicting saltatory states. In the experiment, it can be concluded that the proposed method is better than classical condition predictors in machinery condition prognosis.