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 proceedingKonferenceartikel i proceedingForskningpeer 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.

OriginalsprogEngelsk
TitelIECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society
Antal sider6
ForlagIEEE
Publikationsdato18 okt. 2020
Sider417-422
Artikelnummer9255330
ISBN (Trykt)978-1-7281-5415-2
ISBN (Elektronisk)9781728154145
DOI
StatusUdgivet - 18 okt. 2020
Begivenhed46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 - Virtual, Singapore, Singapore
Varighed: 18 okt. 202021 okt. 2020
http://www.conferences.academicjournals.org/cat/physical-sciences/46th-annual-conference-of-the-ieee-industrial-electronics-society

Konference

Konference46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
Land/OmrådeSingapore
ByVirtual, Singapore
Periode18/10/202021/10/2020
SponsorIEEE Industrial Electronics Society (IES), SPECS - Smart Grid + Power Electronics Consortium Singapore, The Institute of Electrical and Electronics Engineers (IEEE)
Internetadresse
NavnProceedings of the Annual Conference of the IEEE Industrial Electronics Society
ISSN1553-572X

Bibliografisk note

Publisher Copyright:
© 2020 IEEE.

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