Turbine Engine Prognostic Monitoring Using Digital Twin Feedback
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Solution Overview
Problem
Turbine engines face high maintenance costs due to the need for skilled personnel and premature replacement of expensive components, with existing monitoring systems failing to provide adequate insight for optimal maintenance scheduling.
Innovation Solution
The implementation of an asset workscope generation system (AWGS) and fielded asset health advisor (FAHA) that utilize computer-generated models, tracking filters, and sensor data to calculate asset health quantifiers and generate workscope recommendations, optimizing maintenance tasks and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional maintenance schedules are used, then turbine engine availability is maintained through regular service, but maintenance costs increase due to premature replacement and skilled personnel requirements
Solution Approach 1:
The system performs preliminary actions by continuously monitoring turbine engine parameters and predicting future health states before actual degradation occurs. The digital twin model simulates future conditions to anticipate maintenance needs, allowing planners to schedule maintenance at optimal times rather than relying on conservative fixed schedules, thereby reducing premature replacements while ensuring availability.
Solution Approach 2:
The system implements feedback by continuously comparing actual sensor data from the turbine engine with predictions from the digital twin model. This feedback loop refines the accuracy of health predictions and enables dynamic adjustment of maintenance schedules. The workscope recommendation system uses this feedback to optimize maintenance timing, reducing costs by avoiding unnecessary early replacements while maintaining engine availability.
2Productivity
If fixed service intervals are implemented, then maintenance tasks are performed at predictable times, but insight into actual component health is insufficient for optimal scheduling
Solution Approach 1:
The digital twin model serves as an intermediary between physical sensor data and maintenance decision-making. It translates raw sensor measurements into predictive health assessments and future state simulations, providing the missing insight into actual component conditions. This intermediary enables optimized maintenance scheduling by bridging the gap between fixed intervals and real-time health understanding.
Solution Approach 2:
The system changes parameters by transitioning from fixed time-based service intervals to condition-based scheduling derived from digital twin predictions. It dynamically adjusts maintenance timing based on simulated future health states and predicted degradation patterns, transforming static schedules into adaptive plans that reflect actual component conditions, thereby improving scheduling efficiency while eliminating information gaps.
3Measurement precision
If comprehensive monitoring systems are deployed, then better health insights are obtained, but system complexity and implementation costs increase
Solution Approach 1:
The system uses copying by creating a virtual digital twin that replicates the physical turbine engine's behavior and degradation patterns. This copy enables comprehensive health monitoring and predictive analysis without adding physical monitoring complexity to the actual engine. The digital twin serves as a computational model that captures complex interactions and degradation mechanisms, providing high measurement precision while keeping the physical system unchanged.
Solution Approach 2:
The system replaces complex physical monitoring infrastructure with computational modeling approaches. Instead of deploying extensive sensor arrays and complex hardware systems, it uses software-based digital twin simulations to achieve comprehensive health insights. This substitution of mechanical/physical systems with computational methods reduces implementation complexity while maintaining or improving measurement precision through sophisticated algorithms.
4Loss of time
If proactive maintenance planning is implemented, then downtime is reduced through optimized scheduling, but advanced health prediction capabilities are required
Solution Approach 1:
The system performs preliminary actions by predicting future health states and identifying optimal maintenance windows before actual degradation occurs. The digital twin simulates future conditions to anticipate when maintenance will be needed, allowing planners to schedule downtime in advance at optimal times rather than responding to failures or following fixed schedules, thereby reducing actual maintenance downtime.
Solution Approach 2:
The system implements dynamics by transitioning from static maintenance schedules to adaptive, dynamically adjusted planning based on real-time sensor data and digital twin predictions. The workscope recommendation system continuously updates maintenance timing recommendations as new data becomes available and as the digital twin refines its predictions, enabling flexible optimization of downtime while managing the complexity of automated prediction through iterative improvement.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to perform prognostic health monitoring of a turbine engine. An example apparatus includes a health quantifier calculator to execute a computer-generated model to generate first sensor data of a turbine engine, the first sensor data based on simulating a sensor monitoring the turbine engine using asset monitoring information, a parameter tracker to execute a tracking filter using the first sensor data and second sensor data to generate third sensor data corresponding to the turbine engine, the second sensor data based on obtaining sensor data from a sensor monitoring the turbine engine, the third sensor data based on comparing the first sensor data to the second sensor data, the health quantifier calculator to execute the computer-generated model using the third sensor data to generate an asset health quantifier of the turbine engine; and a report generator to generate a report including the asset health quantifier and a workscope recommendation based on the asset health quantifier when the asset health quantifier satisfies a threshold.