Gas Turbine Maintenance Scheduling Using Bayesian Inference
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Solution Overview
Problem
Existing methods for scheduling repairs and maintenance of gas turbines rely on fleet models, which may not accurately reflect the unique conditions and operational histories of individual turbines, leading to suboptimal maintenance intervals and increased risk of unplanned shutdowns.
Innovation Solution
A system and method that uses a processor to incorporate unit-specific data into a fleet model, applying multilevel stochastic modeling and Bayesian inference to project maintenance schedules and adjust for individual turbine conditions, thereby optimizing repair and maintenance intervals based on actual performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fleet models are used to schedule repairs and maintenance, then maintenance can be scheduled based on statistical analysis of comparable gas turbines, but the accuracy of maintenance scheduling deteriorates because individual turbine variations (configuration, manufacturing tolerances, assembly differences, operational histories) are not accounted for
Solution Approach 1:
The patent segments the fleet model approach by creating individual turbine-specific models that incorporate unit-specific data. Instead of treating all turbines uniformly, the system divides the fleet into individual units, each with its own projected parameter information based on unique characteristics such as configuration, manufacturing tolerances, assembly variations, and operational history. This segmentation allows maintenance scheduling to be tailored to each turbine's actual condition rather than relying on generic fleet averages.
Solution Approach 2:
The patent applies local quality by customizing maintenance schedules for each individual turbine based on its specific characteristics and actual performance data. Each turbine receives a tailored maintenance schedule that reflects its unique wear patterns, operational conditions, and historical data, rather than applying a uniform schedule across the entire fleet. This localized approach improves scheduling accuracy by accounting for local variations in turbine behavior.
2Productivity
If fleet model projections are used, then maintenance intervals can be established through statistical analysis, but the risk of unplanned shutdowns increases because individual turbine conditions and wear patterns are not accurately reflected
Solution Approach 1:
The patent implements feedback by continuously incorporating actual turbine data into the projection models. The system uses real-world performance data, operational histories, and maintenance records to update and refine individual turbine models. This feedback loop ensures that projected parameter information accurately reflects actual turbine conditions, enabling more reliable prediction of when maintenance will be needed and reducing the risk of unplanned shutdowns.
Solution Approach 2:
The patent applies preliminary action by using projected parameter information to predict future turbine conditions and schedule maintenance before actual failures occur. The system proactively identifies potential issues by analyzing trends in operational data and projecting future parameter values, allowing maintenance to be performed at optimal intervals that prevent unplanned shutdowns while avoiding unnecessary maintenance activities.
3Ease of operation
If generic fleet models are applied to all turbines, then maintenance scheduling can be standardized across the fleet, but the ability to optimize maintenance intervals for individual turbine conditions is lost
Solution Approach 1:
The patent applies dynamics by creating adaptive maintenance schedules that can change and evolve based on individual turbine performance. Rather than static, fixed schedules, the system dynamically adjusts maintenance intervals for each turbine based on real-time data, operational conditions, and projected parameter information. This dynamic approach maintains ease of operation through automated calculations while providing the adaptability needed to optimize each turbine's maintenance schedule to its specific conditions.
Data Source
AI summary
A system for monitoring a gas turbine includes a memory containing information from comparable gas turbines and an input device that generates a unit data signal and a risk signal. A processor in communication with the memory and the input device incorporates the unit data signal into the database, projects information for the gas turbine, and calculates a conditional risk that the gas turbine will reach a limit. An output signal includes repair or maintenance schedules. A method for monitoring a gas turbine includes receiving information from comparable gas turbines, adding information from the gas turbine to the information from comparable gas turbines, and projecting information for the gas turbine. The method further includes calculating a conditional risk that the gas turbine will reach a limit and generating an output signal containing repair or maintenance schedules.