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

• 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
• Institution: Universiti Malaya

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.