Fault isolation based on online sparse optimization of streaming faulty data

Wenqing Li; Yue Wang; Saif Eddin Jabari

doi:10.1109/CDC40024.2019.9030213

Fault isolation based on online sparse optimization of streaming faulty data

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

Abstract

This paper proposes an online sparse optimization algorithm for fault isolation, which is of a great demand to ensure the normal operation of industrial processes. The proposed method can identify faulty variables without resorting to the historical normal process data. The task of faulty variable location is achieved via performing a sparse matrix decomposition technique on the streaming faulty data, from which a sparse matrix containing fault information is generated and can be further used for pinpointing faulty variables. Additionally, given that process characteristics will change as time goes by, the above decomposition is realized in an online recursive fashion. The efficacy of the proposed method is verified by the Tennessee Eastman benchmark process.

Original language	English (US)
Title of host publication	2019 IEEE 58th Conference on Decision and Control, CDC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2934-2939
Number of pages	6
ISBN (Electronic)	9781728113982
DOIs	https://doi.org/10.1109/CDC40024.2019.9030213
State	Published - Dec 2019
Event	58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France Duration: Dec 11 2019 → Dec 13 2019

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2019-December
ISSN (Print)	0743-1546

Conference

Conference	58th IEEE Conference on Decision and Control, CDC 2019
Country/Territory	France
City	Nice
Period	12/11/19 → 12/13/19

Keywords

Data-driven analysis
Fault isolation
Industrial process
Online sparse and low-rank decomposition

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC40024.2019.9030213

Cite this

Li, W., Wang, Y., & Jabari, S. E. (2019). Fault isolation based on online sparse optimization of streaming faulty data. In 2019 IEEE 58th Conference on Decision and Control, CDC 2019 (pp. 2934-2939). Article 9030213 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2019-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC40024.2019.9030213

Fault isolation based on online sparse optimization of streaming faulty data. / Li, Wenqing; Wang, Yue; Jabari, Saif Eddin.
2019 IEEE 58th Conference on Decision and Control, CDC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2934-2939 9030213 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2019-December).

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

Li, W, Wang, Y & Jabari, SE 2019, Fault isolation based on online sparse optimization of streaming faulty data. in 2019 IEEE 58th Conference on Decision and Control, CDC 2019., 9030213, Proceedings of the IEEE Conference on Decision and Control, vol. 2019-December, Institute of Electrical and Electronics Engineers Inc., pp. 2934-2939, 58th IEEE Conference on Decision and Control, CDC 2019, Nice, France, 12/11/19. https://doi.org/10.1109/CDC40024.2019.9030213

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abstract = "This paper proposes an online sparse optimization algorithm for fault isolation, which is of a great demand to ensure the normal operation of industrial processes. The proposed method can identify faulty variables without resorting to the historical normal process data. The task of faulty variable location is achieved via performing a sparse matrix decomposition technique on the streaming faulty data, from which a sparse matrix containing fault information is generated and can be further used for pinpointing faulty variables. Additionally, given that process characteristics will change as time goes by, the above decomposition is realized in an online recursive fashion. The efficacy of the proposed method is verified by the Tennessee Eastman benchmark process.",

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Fault isolation based on online sparse optimization of streaming faulty data

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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