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

doi:10.1109/peac56338.2022.9959361

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

Mathematics, Physics and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC)
Place of Publication	Piscataway, US
Publisher	IEEE
Pages	284-289
Number of pages	6
ISBN (Electronic)	9781665491426
ISBN (Print)	9781665491419
DOIs	https://doi.org/10.1109/peac56338.2022.9959361
Publication status	Published - 4 Nov 2022
Event	2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC) - Guangzhou, China Duration: 4 Nov 2022 → 7 Dec 2022 http://www.peac-conf.org/

Conference

Conference	2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC)
Country/Territory	China
City	Guangzhou
Period	4/11/22 → 7/12/22
Internet address	http://www.peac-conf.org/

Keywords

data-driven
gap metric
fault diagnosis
DC-DC converter

Access to Document

10.1109/peac56338.2022.9959361

Cite this

@inproceedings{abf33d9da86e46659791a593951882ca,

title = "Data-Driven Gap Metric Fault Diagnosis Technology With Its Application to DC-DC Converter",

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.",

keywords = "data-driven, gap metric, fault diagnosis, DC-DC converter",

author = "Hailang Jin and Zhiwei Gao and Zhiqiang Zuo and Aihua Zhang and Yijing Wang and Haimeng Wu",

note = "Funding information: This work was supported by the China Scholarship Council, the National Natural Science Foundation of China (61673074), and the Tianjin Research Innovation Project for Postgraduate Students (2021YJSB142).; 2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC) ; Conference date: 04-11-2022 Through 07-12-2022",

year = "2022",

month = nov,

day = "4",

doi = "10.1109/peac56338.2022.9959361",

language = "English",

isbn = "9781665491419",

pages = "284--289",

booktitle = "2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC)",

publisher = "IEEE",

address = "United States",

url = "http://www.peac-conf.org/",

}

Jin, H, Gao, Z, Zuo, Z, Zhang, A, Wang, Y & Wu, H 2022, Data-Driven Gap Metric Fault Diagnosis Technology With Its Application to DC-DC Converter. in 2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC). IEEE, Piscataway, US, pp. 284-289, 2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC), Guangzhou, China, 4/11/22. https://doi.org/10.1109/peac56338.2022.9959361

Data-Driven Gap Metric Fault Diagnosis Technology With Its Application to DC-DC Converter. / Jin, Hailang; Gao, Zhiwei; Zuo, Zhiqiang et al.
2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC). Piscataway, US: IEEE, 2022. p. 284-289.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data-Driven Gap Metric Fault Diagnosis Technology With Its Application to DC-DC Converter

AU - Jin, Hailang

AU - Gao, Zhiwei

AU - Zuo, Zhiqiang

AU - Zhang, Aihua

AU - Wang, Yijing

AU - Wu, Haimeng

N1 - Funding information: This work was supported by the China Scholarship Council, the National Natural Science Foundation of China (61673074), and the Tianjin Research Innovation Project for Postgraduate Students (2021YJSB142).

PY - 2022/11/4

Y1 - 2022/11/4

N2 - 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.

AB - 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.

KW - data-driven

KW - gap metric

KW - fault diagnosis

KW - DC-DC converter

U2 - 10.1109/peac56338.2022.9959361

DO - 10.1109/peac56338.2022.9959361

M3 - Conference contribution

SN - 9781665491419

SP - 284

EP - 289

BT - 2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC)

PB - IEEE

CY - Piscataway, US

T2 - 2022 IEEE International Power Electronics and Application Conference and Exposition (PEAC)

Y2 - 4 November 2022 through 7 December 2022

ER -

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

Abstract

Conference

Keywords

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