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The nuclear industry&#x2019;s economic viability is challenged by significant operations and maintenance (O&#x0026;M) costs. Although maintenance strategies are often risk-averse, many maintenance programs rely on schedule-based strategies that perform repairs and replacements regardless of the asset&#x2019;s condition, leading to unnecessary repairs and high costs. Predictive maintenance can help alleviate these costs through condition monitoring and risk-informed decision making. In this article, we show that the use of improved reliability models can help reduce the total cost of ownership (TCO) for a high-value repairable asset. Current risk-informed methods used in the industry today rely on mean-time-between-failure (MTBF) models that may oversimplify failure likelihood estimation. Improvements can be made by integrating condition monitoring, operational history, and maintenance effectiveness into a hybrid reliability model. In contrast with conventional MTBF methods, the generalized renewal process uses recurrent event analysis and historical repair data to quantify the effectiveness of maintenance repairs and estimate the likelihood of failure. During a case study on a nuclear power plant&#x2019;s circulating water system, a hybrid reliability model was fitted to the historical data and shown to have improved likelihood estimations when compared to a MTBF model. Monte Carlo simulations were then used to simulate and compare TCO for various maintenance strategies, showing that an extended replacement interval can reduce overall costs by upwards of 10.7%. The successful results of the improved reliability models showcase the ability to aid decision-making and reduce overall operations and maintenance costs in the nuclear power industry.

A Hybrid Reliability Model using Generalized Renewal Processes for Predictive Maintenance in Nuclear Power Plant Circulating Water Systems