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A systematic literature review of unsupervised fault detection approach for complex engineering system.

Monitoring complex engineering systems is an important countermeasure in managing the risk of faulty events. Observing the response of each process flow will avoid further damages in the production cycle. Both fault-tolerant approach that bears with faulty events and scheduled maintenance that helps...

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Main Authors: Mohd. Sobran, Nur Maisarah, Ismail, Zool Hilmi
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/106012/1/NurMaisarahMohdSobran2023_ASystematicLiteratureReviewofUnsupervisedFaultDetection.pdf
http://eprints.utm.my/106012/
http://dx.doi.org/10.37934/aram.103.1.4360
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spelling my.utm.1060122024-05-29T06:31:19Z http://eprints.utm.my/106012/ A systematic literature review of unsupervised fault detection approach for complex engineering system. Mohd. Sobran, Nur Maisarah Ismail, Zool Hilmi TA Engineering (General). Civil engineering (General) Monitoring complex engineering systems is an important countermeasure in managing the risk of faulty events. Observing the response of each process flow will avoid further damages in the production cycle. Both fault-tolerant approach that bears with faulty events and scheduled maintenance that helps to reduce tool wearing are deeply involved in condition-based monitoring methods implemented in factories. Thus, identification of faulty equipment is need to avoid major breakdown in the production system. A classification framework shows good performance in classifying faulty events, but a labelled dataset is usually financially consuming. Machine learning (ML) techniques have become a prospective tool in the unsupervised fault detection (UFD) approach to prevent total failures in complex engineering system. However, the efficiency of UFD applications, on the other hand, is determined by the selected ML method. This paper presents a systematic literature review of ML methods applied for UFD, highlighting the methods explored in this field and the success of today's state-of-the-art machine learning techniques. This review focuses on the Scopus scientific database and provides a useful information on ML techniques, challenges and opportunities, and new research works in the UFD field. Semarak Ilmu Publishing 2023-03 Article PeerReviewed application/pdf en http://eprints.utm.my/106012/1/NurMaisarahMohdSobran2023_ASystematicLiteratureReviewofUnsupervisedFaultDetection.pdf Mohd. Sobran, Nur Maisarah and Ismail, Zool Hilmi (2023) A systematic literature review of unsupervised fault detection approach for complex engineering system. Journal of Advanced Research in Applied Mechanics, 103 (1). pp. 43-60. ISSN 2289-7895 http://dx.doi.org/10.37934/aram.103.1.4360 DOI: 10.37934/aram.103.1.4360
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Mohd. Sobran, Nur Maisarah
Ismail, Zool Hilmi
A systematic literature review of unsupervised fault detection approach for complex engineering system.
description Monitoring complex engineering systems is an important countermeasure in managing the risk of faulty events. Observing the response of each process flow will avoid further damages in the production cycle. Both fault-tolerant approach that bears with faulty events and scheduled maintenance that helps to reduce tool wearing are deeply involved in condition-based monitoring methods implemented in factories. Thus, identification of faulty equipment is need to avoid major breakdown in the production system. A classification framework shows good performance in classifying faulty events, but a labelled dataset is usually financially consuming. Machine learning (ML) techniques have become a prospective tool in the unsupervised fault detection (UFD) approach to prevent total failures in complex engineering system. However, the efficiency of UFD applications, on the other hand, is determined by the selected ML method. This paper presents a systematic literature review of ML methods applied for UFD, highlighting the methods explored in this field and the success of today's state-of-the-art machine learning techniques. This review focuses on the Scopus scientific database and provides a useful information on ML techniques, challenges and opportunities, and new research works in the UFD field.
format Article
author Mohd. Sobran, Nur Maisarah
Ismail, Zool Hilmi
author_facet Mohd. Sobran, Nur Maisarah
Ismail, Zool Hilmi
author_sort Mohd. Sobran, Nur Maisarah
title A systematic literature review of unsupervised fault detection approach for complex engineering system.
title_short A systematic literature review of unsupervised fault detection approach for complex engineering system.
title_full A systematic literature review of unsupervised fault detection approach for complex engineering system.
title_fullStr A systematic literature review of unsupervised fault detection approach for complex engineering system.
title_full_unstemmed A systematic literature review of unsupervised fault detection approach for complex engineering system.
title_sort systematic literature review of unsupervised fault detection approach for complex engineering system.
publisher Semarak Ilmu Publishing
publishDate 2023
url http://eprints.utm.my/106012/1/NurMaisarahMohdSobran2023_ASystematicLiteratureReviewofUnsupervisedFaultDetection.pdf
http://eprints.utm.my/106012/
http://dx.doi.org/10.37934/aram.103.1.4360
_version_ 1800714786209529856
score 13.211869

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