Technical gazette, Vol. 24 No. 3, 2017.
Original scientific paper
https://doi.org/10.17559/TV-20160517081755
Fault prognostic based on AR-LSSVR for electrolytic capacitor
Ming Yin
; College of Electronic Engineering, Naval University of Engineering, Jiefang Dadao No. 717, Qiaokou district, 430033 Wuhan, Hubei Province, China
Yanyi Xu
; College of Electronic Engineering, Naval University of Engineering, Jiefang Dadao No. 717, Qiaokou district, 430033 Wuhan, Hubei Province, China
Xiaohui Ye
; College of Electronic Engineering, Naval University of Engineering, Jiefang Dadao No. 717, Qiaokou district, 430033 Wuhan, Hubei Province, China
Shaochang Chen
; College of Electronic Engineering, Naval University of Engineering, Jiefang Dadao No. 717, Qiaokou district, 430033 Wuhan, Hubei Province, China
Hongxia Wang
; College of Electronic Engineering, Naval University of Engineering, Jiefang Dadao No. 717, Qiaokou district, 430033 Wuhan, Hubei Province, China
Feng Xie
; College of Electronic Engineering, Naval University of Engineering, Jiefang Dadao No. 717, Qiaokou district, 430033 Wuhan, Hubei Province, China
Abstract
This paper puts forward a method of fault prognostic based on Autoregressive - Support Vector Regression Method (AR-LSSVR) for electrolytic capacitor. Because the electrolytic capacitor is low in cost and large in volume, it is widely used in power electronic circuits. Firstly it introduces the basic model and the fault prognostic algorithm of the AR, LSSVM and AR-LSSVR. The AR-LSSVR prediction model combines the prediction algorithm advantage of the LSSVR and the AR model and complements the two to enhance prediction accuracy. It introduces the flow chart of fault trend prediction based on AR-LSSVR. Finally, the AR-LSSVR model is applied to the Buck circuit. The results indicate that the AR-LSSVR model performs better in trend prediction of electrolytic capacitor.
Keywords
AR-LSSVR; electrolytic capacitor; fault prognosis
Hrčak ID:
183038
URI
Publication date:
15.6.2017.
Visits: 1.495 *