An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process

Majid Ghaniee Zarch; Mohsen Soltani

doi:10.1109/IECON43393.2020.9255330

An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process

Majid Ghaniee Zarch, Mohsen Soltani

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

1 Citationer (Scopus)

Abstract

An effective fault diagnosis scheme can improve system's safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-to-end structure with 13 layers that takes raw sensor's data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.

Originalsprog	Engelsk
Titel	IECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society
Antal sider	6
Forlag	IEEE (Institute of Electrical and Electronics Engineers)
Publikationsdato	18 okt. 2020
Sider	417-422
Artikelnummer	9255330
ISBN (Trykt)	978-1-7281-5415-2
ISBN (Elektronisk)	9781728154145
DOI	https://doi.org/10.1109/IECON43393.2020.9255330
Status	Udgivet - 18 okt. 2020
Begivenhed	46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 - Virtual, Singapore, Singapore Varighed: 18 okt. 2020 → 21 okt. 2020 http://www.conferences.academicjournals.org/cat/physical-sciences/46th-annual-conference-of-the-ieee-industrial-electronics-society

Konference

Konference	46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
Land/Område	Singapore
By	Virtual, Singapore
Periode	18/10/2020 → 21/10/2020
Sponsor	IEEE Industrial Electronics Society (IES), SPECS - Smart Grid + Power Electronics Consortium Singapore, The Institute of Electrical and Electronics Engineers (IEEE)
Internetadresse	http://www.conferences.academicjournals.org/cat/physical-sciences/46th-annual-conference-of-the-ieee-industrial-electronics-society

Navn	Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
ISSN	1553-572X

Bibliografisk note

Publisher Copyright:
© 2020 IEEE.

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10.1109/IECON43393.2020.9255330

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Citationsformater

Zarch, Majid Ghaniee ; Soltani, Mohsen. / An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process. IECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers), 2020. s. 417-422 (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society).

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title = "An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process",

abstract = "An effective fault diagnosis scheme can improve system's safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-to-end structure with 13 layers that takes raw sensor's data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.",

keywords = "Artificial intelligence, Convolutional neural network, Deep learning, Fault detection and isolation, Sensor data, Tennessee Eastman process",

author = "Zarch, {Majid Ghaniee} and Mohsen Soltani",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 ; Conference date: 18-10-2020 Through 21-10-2020",

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doi = "10.1109/IECON43393.2020.9255330",

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publisher = "IEEE (Institute of Electrical and Electronics Engineers)",

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Zarch, MG & Soltani, M 2020, An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process. i IECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society., 9255330, IEEE (Institute of Electrical and Electronics Engineers), Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, s. 417-422, 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020, Virtual, Singapore, Singapore, 18/10/2020. https://doi.org/10.1109/IECON43393.2020.9255330

An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process. / Zarch, Majid Ghaniee; Soltani, Mohsen.
IECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers), 2020. s. 417-422 9255330 (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

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T1 - An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process

AU - Zarch, Majid Ghaniee

AU - Soltani, Mohsen

PY - 2020/10/18

Y1 - 2020/10/18

N2 - An effective fault diagnosis scheme can improve system's safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-to-end structure with 13 layers that takes raw sensor's data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.

AB - An effective fault diagnosis scheme can improve system's safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-to-end structure with 13 layers that takes raw sensor's data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.

KW - Artificial intelligence

KW - Convolutional neural network

KW - Deep learning

KW - Fault detection and isolation

KW - Sensor data

KW - Tennessee Eastman process

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Zarch MG, Soltani M. An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process. I IECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). 2020. s. 417-422. 9255330. (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society). doi: 10.1109/IECON43393.2020.9255330

An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process

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