Data-Driven Gap Metric Fault Diagnosis Technology With Its Application to DC-DC Converter

Hailang Jin, Zhiwei Gao, Zhiqiang Zuo, Aihua Zhang, Yijing Wang, Haimeng Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper investigates a data-driven gap metric fault detection and isolation method for buck DC-DC converters with component faults. First, the averaged state space model of a buck DC-DC converter and its component (inductor, capacitor and load resistance) fault models are established. Second, a data-driven gap metric using subspace identification is utilized to detect the occurred component faults. Third, to isolate these faults, the concept of fault cluster is firstly developed and then the definition of fault isolation under gap matric is proposed. Based on it, a fault isolation condition is presented by solving its fault cluster center model and radius. Finally, the simulation and experiment are reported to demonstrate the effectiveness of the used method.
Original languageEnglish
Title of host publication2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC)
Place of PublicationPiscataway, US
PublisherIEEE
Pages284-289
Number of pages6
ISBN (Electronic)9781665491426
ISBN (Print)9781665491419
DOIs
Publication statusPublished - 4 Nov 2022
Event2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC) - Guangzhou, China
Duration: 4 Nov 20227 Dec 2022
http://www.peac-conf.org/

Conference

Conference2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC)
Country/TerritoryChina
CityGuangzhou
Period4/11/227/12/22
Internet address

Fingerprint

Dive into the research topics of 'Data-Driven Gap Metric Fault Diagnosis Technology With Its Application to DC-DC Converter'. Together they form a unique fingerprint.

Cite this