Severity-aware, age-based preventive maintenance optimization under a virtual-age NHPP: evidence from an underground LHD fleet

Purpose Preventive maintenance (PM) policies for repairable systems often assume constant effectiveness and uniform failure consequences. This paper integrates age-dependent PM effectiveness with severity-aware estimation to produce availability-optimizing PM schedules that are both condition- and consequence-sensitive. Design/methodology/approach We develop a virtual-age non-homogeneous Poisson process model in which each PM type \(k\) induces an age-dependent restoration factor φk(t) ∈ [0,1]. Corrective-maintenance (CM) failures enter the likelihood with downtime-derived weights f_c, yielding a severity-weighted pseudo-likelihood. Two calibrated baselines are estimated at OEM intervals: (1) unweighted (OEM) and (2) severity-aware (weighted). We then optimize PM schedules under both unweighted and weighted φk(t) using a genetic algorithm, with feasibility bounds subject to operational constraints t1 ∈ [200,300]\), t2 ∈ [450,550]\), t3 ∈ [800,1000]\) hours. Findings Relative to OEM estimation, severity-aware fitting increases inferred failure intensity and systematically lowers φk(t) for light and moderate PMs, producing more conservative reliability and improved model fit. Incorporating age-dependent φk(t) reshapes the optimization landscape: weighted schedules favor slightly earlier moderate PM (Type II) while maintaining comparable availability. Back-testing shows closer alignment between predicted and observed failure trajectories, particularly for high-downtime events, demonstrating the value of consequence-aware, age-based PM modeling beyond availability alone. Research limitations/implications Severity is proxied by downtime; multidimensional consequence metrics, i.e. safety, cost, quality and schedule, are future work. Sparse counts at extreme ages can widen uncertainty in φk(t). Unit heterogeneity suggests hierarchical extensions. Practical implications The method operationalizes risk-sensitive PM: planners can jointly tune timing and consequence-aware effectiveness to reduce severe disruptions without over-servicing. The approach is data-driven and CMMS-ready. Originality/value This is the first synthesis to jointly (1) model PM effectiveness as a function of age and (2) estimate it via a severity-weighted virtual-age likelihood, then (3) optimize schedules under real feasibility constraints using real downtimes in chronological order.