Predictive maintenance using digital twins: A systematic literature review

• Published in: Information and software technology Vol. 151; p. 107008
• Main Authors: van Dinter, Raymon, Tekinerdogan, Bedir, Catal, Cagatay
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2022
• Subjects: Active learning; Digital twin; Predictive maintenance; Systematic literature review; Digital twin; Active learning; Systematic literature review; Predictive maintenance
• ISSN: 0950-5849; 1873-6025
• DOI: 10.1016/j.infsof.2022.107008

•The first SLR in predictive maintenance using Digital Twins.•42 primary studies were analyzed.•Key questions for designing a predictive maintance model were answered.•Key challenges were presented in the study. Predictive maintenance is a technique for creating a more sustainable, safe, and profitable industry. One of the key challenges for creating predictive maintenance systems is the lack of failure data, as the machine is frequently repaired before failure. Digital Twins provide a real-time representation of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use. Since 2018, scientific literature on the utilization of Digital Twins for predictive maintenance has accelerated, indicating the need for a thorough review. This research aims to gather and synthesize the studies that focus on predictive maintenance using Digital Twins to pave the way for further research. A systematic literature review (SLR) using an active learning tool is conducted on published primary studies on predictive maintenance using Digital Twins, in which 42 primary studies have been analyzed. This SLR identifies several aspects of predictive maintenance using Digital Twins, including the objectives, application domains, Digital Twin platforms, Digital Twin representation types, approaches, abstraction levels, design patterns, communication protocols, twinning parameters, and challenges and solution directions. These results contribute to a Software Engineering approach for developing predictive maintenance using Digital Twins in academics and the industry. This study is the first SLR in predictive maintenance using Digital Twins. We answer key questions for designing a successful predictive maintenance model leveraging Digital Twins. We found that to this day, computational burden, data variety, and complexity of models, assets, or components are the key challenges in designing these models.