Gas Turbine Engine Condition Monitoring for Workscope Planning
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Solution Overview
Problem
Current gas turbine engine maintenance planning is inefficient due to reliance on service bulletins, life-limited parts, and manual analysis of scattered data sources, leading to suboptimal workscopes and reliability issues.
Innovation Solution
A digital automated system for gas turbine engine condition monitoring and management that performs real-time data analysis, including on-wing and shop visit performance analyses, to generate optimized maintenance recommendations and forecasts, using a digital thread and twin process for repeatable analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of scattered data sources is used, then maintenance planning can be performed, but efficiency is reduced and suboptimal workscopes result
Solution Approach 1:
The patent combines multiple scattered data sources (engine performance data, maintenance history, operational parameters) into a unified digital thread that feeds the automated analysis system. This merging eliminates the inefficiency of manual data collection while maintaining comprehensive data integration.
Solution Approach 2:
The patent introduces a digital twin as an intermediary between physical engine data and maintenance decision-making. The digital twin models engine behavior and predicts maintenance needs, serving as a mediator that translates complex scattered data into actionable maintenance recommendations.
2Reliability
If predefined maintenance intervals are used, then maintenance scheduling is simplified, but reliability issues may occur due to suboptimal workscopes
Solution Approach 1:
The patent transitions from static predefined maintenance intervals to dynamic, condition-based maintenance scheduling. The system continuously analyzes real-time engine data and adjusts maintenance timing based on actual engine condition, improving reliability while the automated system maintains operational simplicity.
Solution Approach 2:
The patent implements a feedback loop where engine performance data continuously feeds the analysis system, which then adjusts maintenance recommendations based on actual engine condition. This closed-loop feedback ensures optimal maintenance timing for reliability while the automated system handles the complexity.
3Productivity
If service bulletins and life-limited parts are relied upon, then maintenance planning can proceed, but optimized workscopes and cost reduction are limited
Solution Approach 1:
The patent replaces traditional mechanical reliance on service bulletins and life-limited parts with an automated digital analysis system. This substitution enables deeper utilization of engine condition information through continuous data analysis, leading to optimized workscopes and reduced costs.
Solution Approach 2:
The patent changes the parameter basis for maintenance from fixed service bulletin intervals and life-limited part thresholds to dynamic parameters derived from real-time engine condition analysis. This allows optimized workscopes that respond to actual engine state rather than predetermined parameters.
Data Source
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AI summary
A method (300) includes receiving (304) engine data associated with an engine. The method further includes analyzing (326) the engine data to determine a number of engine on-wing performance analyses to perform and a number of engine shop visit performance analyses to perform. The method further includes performing (332) an engine on-wing performance analysis. The method further includes performing (334) an engine shop visit performance analysis. The method further includes performing (340) a workscope analysis to generate a recommendation. The method further includes causing the recommendation to be implemented to service the engine.