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Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Background: Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles...

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Main Authors:	Wahab, Nur Haninie Abd, Hasikin, Khairunnisa, Lai, Khin Wee, Xia, Kaijian, Bei, Lulu, Huang, Kai, Wu, Xiang
Format:	Article
Published:	PeerJ 2024
Subjects:	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/45298/ https://doi.org/10.7717/peerj-cs.1943
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spelling	my.um.eprints.452982024-10-07T08:05:20Z http://eprints.um.edu.my/45298/ Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices Wahab, Nur Haninie Abd Hasikin, Khairunnisa Lai, Khin Wee Xia, Kaijian Bei, Lulu Huang, Kai Wu, Xiang QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Background: Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to signi fi cantly improve pro fi tability, safety, and sustainability in various industries. Signi fi cantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the ef fi cacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Speci fi cally, it delves into emerging fi elds in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology: Employing the Preferred Reporting Items for Systematic Review and Meta -Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results: The study revealed four important fi ndings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These fi ndings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies ` fl exibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions: Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to re fi ne PdM strategies and expand the applicability of DT in diverse industrial sectors. PeerJ 2024-04 Article PeerReviewed Wahab, Nur Haninie Abd and Hasikin, Khairunnisa and Lai, Khin Wee and Xia, Kaijian and Bei, Lulu and Huang, Kai and Wu, Xiang (2024) Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices. PeerJ Computer Science, 10. e1943. ISSN 2376-5992, DOI https://doi.org/10.7717/peerj-cs.1943 <https://doi.org/10.7717/peerj-cs.1943>. https://doi.org/10.7717/peerj-cs.1943 10.7717/peerj-cs.1943
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Wahab, Nur Haninie Abd Hasikin, Khairunnisa Lai, Khin Wee Xia, Kaijian Bei, Lulu Huang, Kai Wu, Xiang Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices
description	Background: Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to signi fi cantly improve pro fi tability, safety, and sustainability in various industries. Signi fi cantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the ef fi cacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Speci fi cally, it delves into emerging fi elds in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology: Employing the Preferred Reporting Items for Systematic Review and Meta -Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results: The study revealed four important fi ndings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These fi ndings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies ` fl exibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions: Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to re fi ne PdM strategies and expand the applicability of DT in diverse industrial sectors.
format	Article
author	Wahab, Nur Haninie Abd Hasikin, Khairunnisa Lai, Khin Wee Xia, Kaijian Bei, Lulu Huang, Kai Wu, Xiang
author_facet	Wahab, Nur Haninie Abd Hasikin, Khairunnisa Lai, Khin Wee Xia, Kaijian Bei, Lulu Huang, Kai Wu, Xiang
author_sort	Wahab, Nur Haninie Abd
title	Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices
title_short	Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices
title_full	Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices
title_fullStr	Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices
title_full_unstemmed	Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices
title_sort	systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices
publisher	PeerJ
publishDate	2024
url	http://eprints.um.edu.my/45298/ https://doi.org/10.7717/peerj-cs.1943
_version_	1814047536469508096
score	13.211869

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

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