Predictive Inspection and Maintenance Optimization for Partially Observable Semi-Markov Deteriorating Systems

Abstract

Inspection and maintenance strategies are crucial for ensuring system serviceability. Due to stochastic deterioration and limited observability, sequential decision-making often relies on established approaches such as Partially Observable Markov Decision Processes (POMDPs). However, such approaches impose constraints on periodic decision epochs and exponentially distributed sojourn times, leading to redundant early-stage inspections and increased overall costs. To address these limitations, we propose a novel Predictive Semi-Markov Decision Process (PSMDP) that leverages Remaining Useful Life (RUL) predictions to reduce unnecessary inspections. Our approach incorporates an online scheme that utilizes inspection data for RUL prediction and proactively schedules subsequent maintenance decisions. When applied to infrastructure management, such as bridge maintenance, our PSMDP demonstrates an average cost reduction of 8.9% across various deterioration scenarios compared to other sequential decision-making models. Note to Practitioners—Effective infrastructure maintenance is essential for ensuring public safety and economic growth. However, limited budgets necessitate more efficient strategies. Current periodic inspection strategies often lead to wasteful expenditures due to unnecessary inspections during the early stages of deterioration. To address this challenge, we propose a predictive and proactive approach that leverages Remaining Useful Life (RUL) estimation to schedule inspections and optimize sequential maintenance decisions. By intelligently planning inspections, practitioners can reduce redundant inspections by 89.5% and save overall costs by 8.9% while retaining the required reliability. This approach facilitates a paradigm shift from condition-based maintenance to predictive maintenance.