Maintenance Strategy Optimization for Power Plant Auxiliary Systems Using Reliability-Centered Maintenance and Stochastic Failure Modeling

Abstract

Auxiliary systems play a critical role in the reliable and safe operation of power plants, yet they remain a major source of unplanned downtime, maintenance cost escalation, and operational risk. Traditional maintenance approaches applied to these systems are often time-based or reactive, providing limited insight into failure behavior and inefficient allocation of maintenance resources. This study proposes an integrated maintenance strategy optimization framework that combines Reliability-Centered Maintenance (RCM) principles with stochastic failure modeling to enhance the reliability and costeffectiveness of power plant auxiliary systems. A quantitative, reliability-based case study approach is adopted, focusing on key auxiliary systems including feedwater pumps, cooling water systems, lubrication units, air compressors, and ash handling systems. RCM analysis is used to identify critical components, dominant failure modes, and failure consequences, while stochastic failure models are developed to estimate failure rates, reliability functions, and system-level reliability profiles. Optimized maintenance intervals and policies are derived by balancing reliability, availability, cost, and risk considerations. The results demonstrate that the integrated RCM stochastic approach significantly improves Mean Time Between Failures (MTBF), system availability, and maintenance cost efficiency compared to conventional maintenance strategies. The study contributes a practical, data-driven framework for maintenance optimization and provides valuable insights for power plant asset management, particularly in environments characterized by aging equipment and resource constraints.