Group machinery intelligent maintenance: Adaptive health prediction and global dynamic maintenance decision-making.

• A global adaptive group maintenance policy for multi-component systems is devised. • Maintenance is delayed to provide sufficient buffer time for resource scheduling. • PdM and OM are integrated dynamically over an infinite time horizon. • A heuristic grouping algorithm is developed to alleviate computation complexity. Intelligent preventive maintenance powered by health data analytics is essential to ensure operation safety and performance of diverse industrial equipment. Despite the rapid advancement of preventive maintenance methodologies in recent several decades, their implementation to global dynamic maintenance of complex systems across infinite time horizon, in particular leveraging real-time prognosis information to realize adaptive group partition and ordering, has been largely an under-explored domain. To this end, this study devises a generic global-dynamic group maintenance policy for multi-component degrading systems. As opposed to existing models, the policy automatically renews the entire system health information instantly upon the completion of each group maintenance task; therefore, it realizes the dynamic union of (a) predictive group maintenance and (b) unplanned opportunistic maintenance over an infinite time horizon for the first time, such that to promote decision precision and efficiency. A dynamic grouping algorithm is designed to explore the structure of optimal maintenance solutions. The applicability is exemplified by comparative experiments that show substantial advantages over heuristic policies. [ABSTRACT FROM AUTHOR]