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
APA 6th Edition Yin, M., Xu, Y., Ye, X., Chen, S., Wang, H. i Xie, F. (2017). Fault prognostic based on AR-LSSVR for electrolytic capacitor. Tehnički vjesnik, 24 (3), 783-789. https://doi.org/10.17559/TV-20160517081755
MLA 8th Edition Yin, Ming, et al. "Fault prognostic based on AR-LSSVR for electrolytic capacitor." Tehnički vjesnik, vol. 24, br. 3, 2017, str. 783-789. https://doi.org/10.17559/TV-20160517081755. Citirano 01.03.2021.
Chicago 17th Edition Yin, Ming, Yanyi Xu, Xiaohui Ye, Shaochang Chen, Hongxia Wang i Feng Xie. "Fault prognostic based on AR-LSSVR for electrolytic capacitor." Tehnički vjesnik 24, br. 3 (2017): 783-789. https://doi.org/10.17559/TV-20160517081755
Harvard Yin, M., et al. (2017). 'Fault prognostic based on AR-LSSVR for electrolytic capacitor', Tehnički vjesnik, 24(3), str. 783-789. https://doi.org/10.17559/TV-20160517081755
Vancouver Yin M, Xu Y, Ye X, Chen S, Wang H, Xie F. Fault prognostic based on AR-LSSVR for electrolytic capacitor. Tehnički vjesnik [Internet]. 2017 [pristupljeno 01.03.2021.];24(3):783-789. https://doi.org/10.17559/TV-20160517081755
IEEE M. Yin, Y. Xu, X. Ye, S. Chen, H. Wang i F. Xie, "Fault prognostic based on AR-LSSVR for electrolytic capacitor", Tehnički vjesnik, vol.24, br. 3, str. 783-789, 2017. [Online]. https://doi.org/10.17559/TV-20160517081755
APA 6th Edition Yin, M., Xu, Y., Ye, X., Chen, S., Wang, H. i Xie, F. (2017). Predviđanje greške primjenom AR-LSSVR za elektrolitički kondenzator. Tehnički vjesnik, 24 (3), 783-789. https://doi.org/10.17559/TV-20160517081755
MLA 8th Edition Yin, Ming, et al. "Predviđanje greške primjenom AR-LSSVR za elektrolitički kondenzator." Tehnički vjesnik, vol. 24, br. 3, 2017, str. 783-789. https://doi.org/10.17559/TV-20160517081755. Citirano 01.03.2021.
Chicago 17th Edition Yin, Ming, Yanyi Xu, Xiaohui Ye, Shaochang Chen, Hongxia Wang i Feng Xie. "Predviđanje greške primjenom AR-LSSVR za elektrolitički kondenzator." Tehnički vjesnik 24, br. 3 (2017): 783-789. https://doi.org/10.17559/TV-20160517081755
Harvard Yin, M., et al. (2017). 'Predviđanje greške primjenom AR-LSSVR za elektrolitički kondenzator', Tehnički vjesnik, 24(3), str. 783-789. https://doi.org/10.17559/TV-20160517081755
Vancouver Yin M, Xu Y, Ye X, Chen S, Wang H, Xie F. Predviđanje greške primjenom AR-LSSVR za elektrolitički kondenzator. Tehnički vjesnik [Internet]. 2017 [pristupljeno 01.03.2021.];24(3):783-789. https://doi.org/10.17559/TV-20160517081755
IEEE M. Yin, Y. Xu, X. Ye, S. Chen, H. Wang i F. Xie, "Predviđanje greške primjenom AR-LSSVR za elektrolitički kondenzator", Tehnički vjesnik, vol.24, br. 3, str. 783-789, 2017. [Online]. https://doi.org/10.17559/TV-20160517081755
Sažetak 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.