Cargando…

Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine

A data-driven prediction method is proposed to predict the soft fault and estimate the service life of a DC–DC-converter circuit. First, based on adaptive online non-bias least-square support-vector machine (AONBLSSVM) and the double-population particle-swarm optimization (DP-PSO), the prediction mo...

Descripción completa

Detalles Bibliográficos
Autores principales: Hou, Yuntao, Wu, Zequan, Cai, Xiaohua, Dong, Zhongge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947525/
https://www.ncbi.nlm.nih.gov/pubmed/35327913
http://dx.doi.org/10.3390/e24030402
_version_ 1784674460305981440
author Hou, Yuntao
Wu, Zequan
Cai, Xiaohua
Dong, Zhongge
author_facet Hou, Yuntao
Wu, Zequan
Cai, Xiaohua
Dong, Zhongge
author_sort Hou, Yuntao
collection PubMed
description A data-driven prediction method is proposed to predict the soft fault and estimate the service life of a DC–DC-converter circuit. First, based on adaptive online non-bias least-square support-vector machine (AONBLSSVM) and the double-population particle-swarm optimization (DP-PSO), the prediction model of the soft fault is established. After analyzing the degradation-failure mechanisms of multiple key components and considering the influence of the co-degradation of these components over time on the performance of the circuit, the output ripple voltage is chosen as the fault-characteristic parameter. Finally, relying on historical output ripple voltages, the prediction model is utilized to gradually deduce the predicted values of the fault-characteristic parameter; further, in conjunction with the circuit-failure threshold, the soft fault and the service life of the circuit can be predicted. In the simulation experiment, (1) a time-series prediction is made for the output ripple voltage using the model proposed herein and the online least-square support-vector machine (OLS-SVM). Comparative analyses of fitting-assessment indicators of the predicted and experimental curves confirm that our model is superior to OLS-SVM in both modeling efficiency and prediction accuracy. (2) The effectiveness of the service life prediction method of the circuit is verified.
format Online
Article
Text
id pubmed-8947525
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-89475252022-03-25 Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine Hou, Yuntao Wu, Zequan Cai, Xiaohua Dong, Zhongge Entropy (Basel) Article A data-driven prediction method is proposed to predict the soft fault and estimate the service life of a DC–DC-converter circuit. First, based on adaptive online non-bias least-square support-vector machine (AONBLSSVM) and the double-population particle-swarm optimization (DP-PSO), the prediction model of the soft fault is established. After analyzing the degradation-failure mechanisms of multiple key components and considering the influence of the co-degradation of these components over time on the performance of the circuit, the output ripple voltage is chosen as the fault-characteristic parameter. Finally, relying on historical output ripple voltages, the prediction model is utilized to gradually deduce the predicted values of the fault-characteristic parameter; further, in conjunction with the circuit-failure threshold, the soft fault and the service life of the circuit can be predicted. In the simulation experiment, (1) a time-series prediction is made for the output ripple voltage using the model proposed herein and the online least-square support-vector machine (OLS-SVM). Comparative analyses of fitting-assessment indicators of the predicted and experimental curves confirm that our model is superior to OLS-SVM in both modeling efficiency and prediction accuracy. (2) The effectiveness of the service life prediction method of the circuit is verified. MDPI 2022-03-13 /pmc/articles/PMC8947525/ /pubmed/35327913 http://dx.doi.org/10.3390/e24030402 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hou, Yuntao
Wu, Zequan
Cai, Xiaohua
Dong, Zhongge
Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine
title Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine
title_full Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine
title_fullStr Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine
title_full_unstemmed Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine
title_short Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine
title_sort prediction method of soft fault and service life of dc-dc-converter circuit based on improved support vector machine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947525/
https://www.ncbi.nlm.nih.gov/pubmed/35327913
http://dx.doi.org/10.3390/e24030402
work_keys_str_mv AT houyuntao predictionmethodofsoftfaultandservicelifeofdcdcconvertercircuitbasedonimprovedsupportvectormachine
AT wuzequan predictionmethodofsoftfaultandservicelifeofdcdcconvertercircuitbasedonimprovedsupportvectormachine
AT caixiaohua predictionmethodofsoftfaultandservicelifeofdcdcconvertercircuitbasedonimprovedsupportvectormachine
AT dongzhongge predictionmethodofsoftfaultandservicelifeofdcdcconvertercircuitbasedonimprovedsupportvectormachine