The advance of digital twin for predictive maintenance: The role and function of machine learning

Abstract

The recent advance of digital twin (DT) has greatly facilitated the development of predictive maintenance (PdM). DT for PdM enables accurate equipment status recognition and proactive fault prediction, enhancing reliability. This shift from reactive to proactive services optimizes maintenance schedules, minimizes downtime, and improves enterprise profitability and competitiveness. However, the research and application of DT for PdM are still in their infancy, probably because the role and function of machine learning (ML) in DT for PdM have not yet been fully investigated by the industry and academia. This paper focuses on a systematic review of the role of ML in DT for PdM and identifies, evaluates and analyses a clear and systematic approach to the published literature relevant to DT and PdM. Subsequently, the state-of-the-art applications of ML in various application areas of DT for PdM are introduced. Finally, the challenges and opportunities of ML for DT-PdM are revealed and discussed. The outcome of this paper can bring tangible benefits to the research and implementation of ML in DT-PdM.