Intelligent spare ordering and replacement optimisation leveraging adaptive prediction information

Predicting system health using inspection technologies is crucial for efficiently managing the maintenance of various industrial products. This study introduced an innovative policy for intelligently ordering and replacing spare parts. It utilises real-time prediction data to make sequential decisions on whether to schedule spares and when to conduct non-immediate maintenance. A generalised non-linear stochastic process was established to capture the underlying degradation path, with a lifetime coefficient updated through Bayesian inference. Conditional reliability assessed during regular inspections determines when spare preparations and delayed replacements are warranted. Predictive replacements can also be postponed based on the expected remaining lifetime adjusted for safety factors and spare lead times. The model dynamically optimises operational costs by iteratively optimising spare-ordering times, postponement intervals, and adjustment coefficients. Numerical experiments on high-speed train gearboxes validated its superior cost-effectiveness compared to conventional approaches.