Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph

Huakun Chen; Weiguo Zhang; Yongxi Lyu

doi:10.1007/978-981-15-8155-7_345

Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph

Huakun Chen
, Weiguo Zhang
, Yongxi Lyu

School of Automation

Northwestern Polytechnical University Xian

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

Abstract

Time fault propagation graph (TFPG) is a directed graph model to describe the dynamic causality of the system. According to TFPG model, the system fault can be inferred from the observed events and the fault time can be estimated. In this paper, the process of fault diagnosis using time fault propagation graph is researched, and TFPG fault diagnosis algorithm framework is designed. In order to improve the effectiveness of fault diagnosis, the improved hypothesis initialization algorithm based on event and improved hypothesis update algorithm based on incremental reasoning are developed in the paper. Finally, we take Right Inboard Power Control Unit (PCU-RIB) as an example. According to the system characteristics and fault types, the TFPG model of PCU-RIB is constructed. Then the improved fault diagnosis algorithm based on TFPG is used for simulation verification. The results show that the improved algorithm has good robustness, can correctly eliminate the interference of false alarms and identify the corresponding fault.

Original language	English
Title of host publication	Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020
Editors	Liang Yan, Haibin Duan, Xiang Yu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	4137-4150
Number of pages	14
ISBN (Print)	9789811581540
DOIs	https://doi.org/10.1007/978-981-15-8155-7_345
State	Published - 2022
Event	International Conference on Guidance, Navigation and Control, ICGNC 2020 - Tianjin, China Duration: 23 Oct 2020 → 25 Oct 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	644 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Guidance, Navigation and Control, ICGNC 2020
Country/Territory	China
City	Tianjin
Period	23/10/20 → 25/10/20

Keywords

Fault diagnosis
Health management
Time consistency
Time fault propagation diagram

Access to Document

10.1007/978-981-15-8155-7_345

Cite this

Chen, H., Zhang, W., & Lyu, Y. (2022). Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph. In L. Yan, H. Duan, & X. Yu (Eds.), Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020 (pp. 4137-4150). (Lecture Notes in Electrical Engineering; Vol. 644 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-8155-7_345

Chen, Huakun ; Zhang, Weiguo ; Lyu, Yongxi. / Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph. Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. editor / Liang Yan ; Haibin Duan ; Xiang Yu. Springer Science and Business Media Deutschland GmbH, 2022. pp. 4137-4150 (Lecture Notes in Electrical Engineering).

@inproceedings{e98b439eea7b4152901099170c0b0798,

title = "Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph",

abstract = "Time fault propagation graph (TFPG) is a directed graph model to describe the dynamic causality of the system. According to TFPG model, the system fault can be inferred from the observed events and the fault time can be estimated. In this paper, the process of fault diagnosis using time fault propagation graph is researched, and TFPG fault diagnosis algorithm framework is designed. In order to improve the effectiveness of fault diagnosis, the improved hypothesis initialization algorithm based on event and improved hypothesis update algorithm based on incremental reasoning are developed in the paper. Finally, we take Right Inboard Power Control Unit (PCU-RIB) as an example. According to the system characteristics and fault types, the TFPG model of PCU-RIB is constructed. Then the improved fault diagnosis algorithm based on TFPG is used for simulation verification. The results show that the improved algorithm has good robustness, can correctly eliminate the interference of false alarms and identify the corresponding fault.",

keywords = "Fault diagnosis, Health management, Time consistency, Time fault propagation diagram",

author = "Huakun Chen and Weiguo Zhang and Yongxi Lyu",

note = "Publisher Copyright: {\textcopyright} 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2022",

doi = "10.1007/978-981-15-8155-7\_345",

language = "英语",

isbn = "9789811581540",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "4137--4150",

editor = "Liang Yan and Haibin Duan and Xiang Yu",

booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020",

}

Chen, H, Zhang, W & Lyu, Y 2022, Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph. in L Yan, H Duan & X Yu (eds), Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. Lecture Notes in Electrical Engineering, vol. 644 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 4137-4150, International Conference on Guidance, Navigation and Control, ICGNC 2020, Tianjin, China, 23/10/20. https://doi.org/10.1007/978-981-15-8155-7_345

Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph. / Chen, Huakun; Zhang, Weiguo ; Lyu, Yongxi.
Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. ed. / Liang Yan; Haibin Duan; Xiang Yu. Springer Science and Business Media Deutschland GmbH, 2022. p. 4137-4150 (Lecture Notes in Electrical Engineering; Vol. 644 LNEE).

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

TY - GEN

T1 - Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph

AU - Chen, Huakun

AU - Zhang, Weiguo

AU - Lyu, Yongxi

PY - 2022

Y1 - 2022

N2 - Time fault propagation graph (TFPG) is a directed graph model to describe the dynamic causality of the system. According to TFPG model, the system fault can be inferred from the observed events and the fault time can be estimated. In this paper, the process of fault diagnosis using time fault propagation graph is researched, and TFPG fault diagnosis algorithm framework is designed. In order to improve the effectiveness of fault diagnosis, the improved hypothesis initialization algorithm based on event and improved hypothesis update algorithm based on incremental reasoning are developed in the paper. Finally, we take Right Inboard Power Control Unit (PCU-RIB) as an example. According to the system characteristics and fault types, the TFPG model of PCU-RIB is constructed. Then the improved fault diagnosis algorithm based on TFPG is used for simulation verification. The results show that the improved algorithm has good robustness, can correctly eliminate the interference of false alarms and identify the corresponding fault.

AB - Time fault propagation graph (TFPG) is a directed graph model to describe the dynamic causality of the system. According to TFPG model, the system fault can be inferred from the observed events and the fault time can be estimated. In this paper, the process of fault diagnosis using time fault propagation graph is researched, and TFPG fault diagnosis algorithm framework is designed. In order to improve the effectiveness of fault diagnosis, the improved hypothesis initialization algorithm based on event and improved hypothesis update algorithm based on incremental reasoning are developed in the paper. Finally, we take Right Inboard Power Control Unit (PCU-RIB) as an example. According to the system characteristics and fault types, the TFPG model of PCU-RIB is constructed. Then the improved fault diagnosis algorithm based on TFPG is used for simulation verification. The results show that the improved algorithm has good robustness, can correctly eliminate the interference of false alarms and identify the corresponding fault.

KW - Fault diagnosis

KW - Health management

KW - Time consistency

KW - Time fault propagation diagram

UR - https://www.scopus.com/pages/publications/85120644794

U2 - 10.1007/978-981-15-8155-7_345

DO - 10.1007/978-981-15-8155-7_345

M3 - 会议稿件

AN - SCOPUS:85120644794

SN - 9789811581540

T3 - Lecture Notes in Electrical Engineering

SP - 4137

EP - 4150

BT - Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020

A2 - Yan, Liang

A2 - Duan, Haibin

A2 - Yu, Xiang

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Guidance, Navigation and Control, ICGNC 2020

Y2 - 23 October 2020 through 25 October 2020

ER -

Chen H, Zhang W , Lyu Y. Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph. In Yan L, Duan H, Yu X, editors, Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. Springer Science and Business Media Deutschland GmbH. 2022. p. 4137-4150. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-8155-7_345

Research and Application of Fault Diagnosis Method Based on Time Fault Propagation Graph

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