Li Yang

This study tackles the challenges of managing the reliability of stochastically degrading mission-critical systems by proposing an integrated lifecycle framework that combines in-warranty collaborative warranty models with out-of-warranty maintenance strategies. At its core lies the condition-based random collaborative warranty with heterogeneous preventive maintenance (CBRCW-HPM), a user-manufacturer collaborative governance mechanism that categorizes system degradation into light, moderate, and failure states. It formulates state transition probabilities along with cost- and time-related metrics and adopts the reinforcement learning–based online Dyna algorithm to provide accurate warranty cost estimation, thereby enabling adaptability to non-unique initial and terminal states. Warranty pricing is innovatively formulated using the Cobb–Douglas production function, treating warranties as independent value-added services. Complementing this, the bivariate condition-based random inspection (BCBRI) strategy leverages in-warranty operational performance data to classify out-of-warranty systems into high- and low-frequency failure types and optimizes long-term cost rates via the renewal reward theorem through scenario-tailored maintenance actions. Numerical results validate the proposed approaches, and case studies further verify that CBRCW-HPM outperforms fixed-interval warranty models, while BCBRI surpasses fixed-interval inspection schemes in adaptability and cost-effectiveness. This integrated solution effectively meets the core requirements of data-driven reliability management for mission-critical systems in the advanced digital technology era.

Existing research on maintenance policy optimization often assumes that the failure intensity functions of the system under maintenance are primarily altered by the effectiveness of maintenance. However, in practical scenarios, these functions can also undergo modifications due to innovation-related replacements. This paper focuses on systems with finite operating lifetimes and under the situation that the precise timing of innovation-related modifications is uncertain. The system undergoes preventive maintenance once it reaches a certain age, and failures occurring between these preventive maintenance actions are addressed through minimal repairs. This paper introduces a comprehensive framework for optimizing both prior and after innovation replacement preventive maintenance policies, with the objective of minimizing the total expected cost. The paper also presents optimal resource allocation strategies for innovation replacement development. Furthermore, we extend the framework to model imperfect preventive maintenance, in which each maintenance action only partially restores the system. Analytical maintenance policy is addressed and numerical examples are provided to illustrate the practical applications of the proposed methodologies. The proposed framework lowers lifecycle maintenance cost and outage time while maintaining reliability. For asset managers, it provides an interpretable, ready-to-implement rule that translates monitoring and cost inputs into executable maintenance schedules and defensible budget plans.

We establish a global adaptive group maintenance policy for multicomponent systems over an infinite time horizon. This process enables the dynamic integration of 1) scheduled group maintenance and 2) unscheduled opportunistic maintenance in a global adaptive manner. Unlike traditional maintenance models that rely on predefined maintenance windows and schedules, our approach continuously adjusts based on the latest operational data, obviating the need to prespecify a scheduling horizon. The group maintenance model is governed by a two-phase scheduling framework: from tentative component age determination to dynamic system group clustering. We provide structural insights into the optimality of system maintenance timing through penalty analysis. Leveraging these findings, we design an efficient heuristic grouping algorithm that transforms the long-term maintenance planning problem into short-term group selection problems, significantly streamlining computational efforts and allowing for adaptive refinement. Comparative experiments on phased-array radar systems demonstrate substantial improvements over existing policies.