MAPTECH: Monitoring And Prediction of Turbine Engine Component Health

Inspection of turbine engine components is an essential part of standard maintenance procedures to ensure the long-term health of aircraft engines. Existing techniques are time-consuming and labor-intensive because they require point-by-point scanning and/or extensive data processing. As a result, these inspections are not cost-effective to conduct, despite being a critical part of long-term sustainment and maintenance. Initial results from Phase I of this R&D program demonstrated that hyperspectral imaging (HSI) in combination with advanced machine learning and robotic automation has the potential to bend the cost curve, making detailed health assessments of aircraft components more cost-effective, improving aircraft reliability and availability, and reducing lifecycle maintenance costs. Intelligent Automation, Inc. (IAI), along with the Resonant Sciences LLC propose to continue developing MAPTECH (Monitoring and Prediction of Turbine Engine Component Health), an automated component inspection system that can significantly decrease engine maintenance time and cost in depot applications. During Phase II, the team will expand upon and extend our research efforts from Phase I and conduct system development efforts focused on transition. IAI will collect additional ground-truth datasets, train a machine learning framework, and demonstrate the ability to estimate fatigue condition on novel test articles or representative engine components.