Continuous-Time Markov Chain Model for Risk-Informed Predictive Maintenance and Cost Benefit Analysis of Selected Assets in Nuclear Power Plants

To achieve high-capacity factors, the nuclear fleet has traditionally relied on labor-intensive, time-consuming operation and preventive maintenance programs for plant systems. The manually performed inspections, calibrations, testing, and maintenance of plant assets at periodic frequencies, along with the time-based replacement of assets irrespective of condition, have resulted in a costly, labor-centric business model. Fortunately, there are technologies (e.g. advanced sensor, data analytics, risk-assessment, and cost benefit analysis methodologies) that can enable the transition to a technology-centric business model. This paper analyzes the applicability of continuous-time Markov chain models to perform a risk-informed cost benefit analysis of a single asset, such as a pump and motor set of the circulating water system of a pressurized water reactor. Two-state and three-state Markov chain homogeneous models are applied to analyze different maintenance scenarios, with the parameters of the models estimated from historical operational data of the reactor. The analysis concluded that the corrective maintenance rate and equipment failure rate are the two most important parameters in terms of the plant’s economic performance. For example, changes in the corrective maintenance rate and the equipment failure rate will change the baseline hourly profits from $30.60 to $33.50, which is close to the maximum possible hourly profit of $34.00. Since the optimization and automation of maintenance activities can be accomplished by transitioning to a risk-informed predictive maintenance (PdM) strategy, this paper also analyzes the risks and cost benefits of introducing a PdM approach for a pump-motor set of the circulating water system. It is concluded that while introducing PdM can be beneficial for the overall economic performance of the plant, careful consideration should be given to the cost of the PdM and the false alarm rate. Different scenarios suggested that for a typical PdM system, an increase of 10% to 30% in the maintenance rate will justify purchasing and operating the system. However, it will depend on the system’s false alarm rate, initial cost, and operation cost. Finally, this paper presents a cost benefit analysis using a nonhomogeneous Markov chain model for the case of motor degradation using the data from a real plant. It is concluded that the nonhomogeneous Markov chain model in combination with PdM strategies can provide valuable insights into a plant’s future risks and cost of operation. The key contributions of this paper are threefold: rigorous analysis of the economic performance of a commercial nuclear reactor under different maintenance scenarios, economic analysis of the benefits and shortcomings of introducing PdM systems, and analysis of the economic performance of a commercial nuclear reactor using real-world data collected during operation.