An online data driven fault detection method in dynamic process based on sparse representation

Rui Yang; Mengjie Huang

doi:10.1109/ICMA.2017.8016093

An online data driven fault detection method in dynamic process based on sparse representation

Rui Yang, Mengjie Huang^*

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

With the development of science and industrial technologies, the intermittent fault has become the main fault of actual system, and the fault diagnosis on intermittent fault has progressed. However, with the increase in the complexity and uncertainty of modern engineering system, it is not feasible to establish accurate mathematical models. Thus, data-driven method is required for fault detection. Based on the sparsity of intermittent faults in some domains, an intermittent fault detection method based on sparse representation is proposed, with the online update of over-complete dictionary and fault detection threshold. With the simulation verification, the proposed method is suitable for intermittent fault detection in dynamic system.

Original language	English
Title of host publication	2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1816-1820
Number of pages	5
ISBN (Electronic)	9781509067572
DOIs	https://doi.org/10.1109/ICMA.2017.8016093
Publication status	Published - 23 Aug 2017
Externally published	Yes
Event	14th IEEE International Conference on Mechatronics and Automation, ICMA 2017 - Takamatsu, Japan Duration: 6 Aug 2017 → 9 Aug 2017

Publication series

Name	2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017

Conference

Conference	14th IEEE International Conference on Mechatronics and Automation, ICMA 2017
Country/Territory	Japan
City	Takamatsu
Period	6/08/17 → 9/08/17

Keywords

Fault detection
Intermittent fault
Sparse representation

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10.1109/ICMA.2017.8016093

Cite this

Yang, R., & Huang, M. (2017). An online data driven fault detection method in dynamic process based on sparse representation. In 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017 (pp. 1816-1820). Article 8016093 (2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMA.2017.8016093

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title = "An online data driven fault detection method in dynamic process based on sparse representation",

abstract = "With the development of science and industrial technologies, the intermittent fault has become the main fault of actual system, and the fault diagnosis on intermittent fault has progressed. However, with the increase in the complexity and uncertainty of modern engineering system, it is not feasible to establish accurate mathematical models. Thus, data-driven method is required for fault detection. Based on the sparsity of intermittent faults in some domains, an intermittent fault detection method based on sparse representation is proposed, with the online update of over-complete dictionary and fault detection threshold. With the simulation verification, the proposed method is suitable for intermittent fault detection in dynamic system.",

keywords = "Fault detection, Intermittent fault, Sparse representation",

author = "Rui Yang and Mengjie Huang",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IEEE International Conference on Mechatronics and Automation, ICMA 2017 ; Conference date: 06-08-2017 Through 09-08-2017",

year = "2017",

month = aug,

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doi = "10.1109/ICMA.2017.8016093",

language = "English",

series = "2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017",

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

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booktitle = "2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017",

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Yang, R & Huang, M 2017, An online data driven fault detection method in dynamic process based on sparse representation. in 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017., 8016093, 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017, Institute of Electrical and Electronics Engineers Inc., pp. 1816-1820, 14th IEEE International Conference on Mechatronics and Automation, ICMA 2017, Takamatsu, Japan, 6/08/17. https://doi.org/10.1109/ICMA.2017.8016093

An online data driven fault detection method in dynamic process based on sparse representation. / Yang, Rui ; Huang, Mengjie.
2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1816-1820 8016093 (2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017).

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

TY - GEN

T1 - An online data driven fault detection method in dynamic process based on sparse representation

AU - Yang, Rui

AU - Huang, Mengjie

PY - 2017/8/23

Y1 - 2017/8/23

N2 - With the development of science and industrial technologies, the intermittent fault has become the main fault of actual system, and the fault diagnosis on intermittent fault has progressed. However, with the increase in the complexity and uncertainty of modern engineering system, it is not feasible to establish accurate mathematical models. Thus, data-driven method is required for fault detection. Based on the sparsity of intermittent faults in some domains, an intermittent fault detection method based on sparse representation is proposed, with the online update of over-complete dictionary and fault detection threshold. With the simulation verification, the proposed method is suitable for intermittent fault detection in dynamic system.

AB - With the development of science and industrial technologies, the intermittent fault has become the main fault of actual system, and the fault diagnosis on intermittent fault has progressed. However, with the increase in the complexity and uncertainty of modern engineering system, it is not feasible to establish accurate mathematical models. Thus, data-driven method is required for fault detection. Based on the sparsity of intermittent faults in some domains, an intermittent fault detection method based on sparse representation is proposed, with the online update of over-complete dictionary and fault detection threshold. With the simulation verification, the proposed method is suitable for intermittent fault detection in dynamic system.

KW - Fault detection

KW - Intermittent fault

KW - Sparse representation

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DO - 10.1109/ICMA.2017.8016093

M3 - Conference Proceeding

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T3 - 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017

SP - 1816

EP - 1820

BT - 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th IEEE International Conference on Mechatronics and Automation, ICMA 2017

Y2 - 6 August 2017 through 9 August 2017

ER -

Yang R , Huang M. An online data driven fault detection method in dynamic process based on sparse representation. In 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1816-1820. 8016093. (2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017). doi: 10.1109/ICMA.2017.8016093

An online data driven fault detection method in dynamic process based on sparse representation

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