Nuclear Facility Maintenance Optimization via Bayesian Failure Prediction
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Solution Overview
Problem
Determining the cost-effectiveness and optimal timing of maintenance activities in nuclear power plants is challenging due to varying failure data from different sources and the criticalities of components, necessitating a method to combine data and predict failures accurately while adhering to budgets.
Innovation Solution
A method that calculates the net present value of maintenance activities by determining the change in probability of component failure, multiplying it with associated losses, and optimizing based on budget constraints, using Bayes' Theorem to combine Weibull probability distributions from multiple data sets, and displaying planned activities graphically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sets of failure data from different sources are combined to improve prediction accuracy, then the reliability of failure prediction is improved, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent combines multiple sets of failure data from different sources (industry data, fleet data, expert data, facility-specific data, manufacturer data) using a probabilistic framework based on Bayes' Theorem. This merging approach integrates diverse data sources to improve prediction accuracy while providing a systematic method to handle the complexity through mathematical combination rules.
Solution Approach 2:
The patent introduces a probabilistic framework and Bayesian synthesis mechanism as an intermediary to process and combine multiple data sources. This intermediary layer transforms raw data from various sources into synthesized failure probability predictions, managing the complexity through standardized mathematical operations rather than direct manual analysis.
2Reliability
If proactive maintenance activities are performed to reduce failure probability, then the reliability of the facility is improved, but the cost of maintenance activities increases
Solution Approach 1:
The patent changes the parameter of maintenance timing from fixed schedules to optimized intervals based on predicted failure probabilities. By calculating the net present value of maintenance activities at different times and selecting the optimal timing, the system achieves improved reliability while minimizing maintenance costs through parameter optimization rather than increased spending.
Solution Approach 2:
The patent performs preliminary maintenance activities based on predicted failure probabilities before actual failures occur. By using failure probability predictions to schedule maintenance in advance, the system prevents failures and improves reliability while avoiding the higher costs associated with reactive maintenance and unplanned outages.
3Productivity
If maintenance activities are optimized based on net present value calculations, then the economic efficiency is improved, but the complexity of economic analysis increases
Solution Approach 1:
The patent replaces complex manual economic analysis with an automated computational system that calculates net present values and optimizes maintenance schedules. The computer-based implementation handles the mathematical complexity of NPV calculations, discount rates, and optimization algorithms, providing economically efficient results without requiring manual execution of complex economic analyses.
4Quantity of substance
If maintenance activities are planned according to budget constraints, then the financial control is improved, but the flexibility in optimizing maintenance timing is reduced
Solution Approach 1:
The patent implements a dynamic maintenance optimization system that adjusts maintenance schedules based on both budget constraints and real-time failure probability predictions. The system dynamically balances financial control with timing flexibility by allowing maintenance activities to be rescheduled within budget parameters based on changing facility conditions and predicted failure risks, rather than following rigid fixed schedules.
Data Source
AI summary
An improved method of selecting and planning the performance of various maintenance activities on a facility such as a nuclear power plant includes determining the net present value of a number of future net savings that are expected to result from performance of the maintenance activity at a given time, and selecting and planning the maintenance activities in a fashion that maximizes net present value. The method includes, for each of a number of components and a number of time periods, determining a change in the probability that a component will fail within a time period, with the change resulting from an assumption that a maintenance activity is performed. The change in probability is multiplied with the losses associated with a failure in order to determine a gross savings from which costs are subtracted to determine net savings. The probabilities of failure may be determined from a probability failure model that has been derived from multiple sets of failure data that are characterized by Weibull distributions and are mathematically combined according to Bayes' Theorem. The maintenance activities may also be optimized according to a number of budget figures. An apparatus for performing the method is also disclosed.