On the requirements of digital twin-driven autonomous maintenance - Khan, Samir; Farnsworth, Michael; McWilliam, Richard; Erkoyuncu, John Ahmet (Elsevier, 2020)
Autonomy has become a focal point for research and development in many industries. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts are now being focused on the sub-system and system-level through advancements in artificial intelligence. Exploiting its benefits requires some innovative thinking to integrate overarching concepts from big data analysis, digitisation, sensing, optimisation, information technology, and systems engineering. With recent developments in Industry 4.0, machine learning and digital twin, there has been a growing interest in adapting these concepts to achieve autonomous maintenance; the automation of predictive maintenance scheduling directly from operational data and for in-built repair at the systems-level. However, there is still ambiguity whether state-of-the-art developments are truly autonomous or they simply automate a process. In light of this, it is important to present the current perspectives about where the technology stands today and indicate possible routes for the future. As a result, this effort focuses on recent trends in autonomous maintenance before moving on to discuss digital twin as a vehicle for decision making from the viewpoint of requirements, whilst the role of AI in assisting with this process is also explored. A suggested framework for integrating digital twin strategies within maintenance models is also discussed. Finally, the article looks towards future directions on the likely evolution and implications for its development as a sustainable technology
Keywords: Reinforcement learning; Fault detection and isolation; Autonomous systems; Digital twin