A mean remaining lifetime model with bivariate-state dependence under a delay-time framework

Source abstract

Abstract Traditional reliability models often overlook condition monitoring (CM) data, resulting in biased predictions and inefficient maintenance planning. This paper introduces a novel, process-based reliability index, the bivariate-state dependent mean remaining lifetime (BSDMRL), for an $n$-component parallel system operating under a delay-time framework. We develop a heuristic method that not only simplifies the computation of the BSDMRL via a more tractable surrogate index but also reveals key monotonicity properties that enable the design of effective preventive maintenance policies. By embedding this reliability index within a threshold-type preventive maintenance policy and a cost-reward optimization model, we jointly determine optimal inspection intervals and maintenance thresholds to minimize the long-run average maintenance cost. Through numerical experiments and a comparative study, we demonstrate that our reliability-driven approach significantly outperforms models that fail to fully integrate CM information, leading to more robust and cost-effective maintenance decisions.