A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance

Hao Luo; Shen Yin; Okyay Kayank

doi:10.1109/SMC.2018.00531

A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance

Hao Luo
, Shen Yin
, Okyay Kayank

School of Astronautics, Harbin Institute of Technology
Bogazici University

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

6 Scopus citations

Abstract

This paper presents the latest study on the data-driven process monitoring system design for the dynamic processes with sinusoidal disturbance. In the previous study, it is understood that the row space of the deterministic disturbance is essential to the subspace method aided data-driven design. Based on the previous study, this paper first determines the row space of sinusoidal disturbance. By projecting the process data into the determined subspaces, the fault detection systems can be designed based on the identified kernel subspace of the system. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3135-3140
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00531
State	Published - 2 Jul 2018
Externally published	Yes
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	7/10/18 → 10/10/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Data-driven
deterministic disturbance
fault detection
subspace method

Access to Document

10.1109/SMC.2018.00531

Cite this

Luo, H., Yin, S., & Kayank, O. (2018). A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 3135-3140). Article 8616528 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00531

Luo, Hao ; Yin, Shen ; Kayank, Okyay. / A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3135-3140 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

@inproceedings{b1172077fee7475baf4a93bea750281e,

title = "A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance",

abstract = "This paper presents the latest study on the data-driven process monitoring system design for the dynamic processes with sinusoidal disturbance. In the previous study, it is understood that the row space of the deterministic disturbance is essential to the subspace method aided data-driven design. Based on the previous study, this paper first determines the row space of sinusoidal disturbance. By projecting the process data into the determined subspaces, the fault detection systems can be designed based on the identified kernel subspace of the system. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.",

keywords = "Data-driven, deterministic disturbance, fault detection, subspace method",

author = "Hao Luo and Shen Yin and Okyay Kayank",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/SMC.2018.00531",

language = "英语",

series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3135--3140",

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address = "美国",

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Luo, H , Yin, S & Kayank, O 2018, A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616528, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 3135-3140, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 7/10/18. https://doi.org/10.1109/SMC.2018.00531

A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance. / Luo, Hao ; Yin, Shen; Kayank, Okyay.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3135-3140 8616528 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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

TY - GEN

T1 - A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance

AU - Luo, Hao

AU - Yin, Shen

AU - Kayank, Okyay

PY - 2018/7/2

Y1 - 2018/7/2

N2 - This paper presents the latest study on the data-driven process monitoring system design for the dynamic processes with sinusoidal disturbance. In the previous study, it is understood that the row space of the deterministic disturbance is essential to the subspace method aided data-driven design. Based on the previous study, this paper first determines the row space of sinusoidal disturbance. By projecting the process data into the determined subspaces, the fault detection systems can be designed based on the identified kernel subspace of the system. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.

AB - This paper presents the latest study on the data-driven process monitoring system design for the dynamic processes with sinusoidal disturbance. In the previous study, it is understood that the row space of the deterministic disturbance is essential to the subspace method aided data-driven design. Based on the previous study, this paper first determines the row space of sinusoidal disturbance. By projecting the process data into the determined subspaces, the fault detection systems can be designed based on the identified kernel subspace of the system. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.

KW - Data-driven

KW - deterministic disturbance

KW - fault detection

KW - subspace method

UR - https://www.scopus.com/pages/publications/85061536230

U2 - 10.1109/SMC.2018.00531

DO - 10.1109/SMC.2018.00531

M3 - 会议稿件

AN - SCOPUS:85061536230

T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

SP - 3135

EP - 3140

BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Y2 - 7 October 2018 through 10 October 2018

ER -

Luo H , Yin S, Kayank O. A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3135-3140. 8616528. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). doi: 10.1109/SMC.2018.00531

A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance

Abstract

Publication series

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UN SDGs

Keywords

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