Advanced Manufacturing Analytics: Optimizing Engine Performance through Real-Time Data and Predictive Maintenance

Abstract

This report proposes a research activity designed to apply advanced analytics to power systems. The objective is to employ recent advances in data analytics to develop an adaptive optimization framework that continuously leverages data to accelerate engine performance optimization. The analytics will address advanced combustion monitoring and real-time diagnostic challenges and will also extend to preventative maintenance strategy development. A combination of physics-based modeling, hardware capabilities, and large data integration will be used to predict engine degradation on a component-specific level and to determine whether life optimization is required. An adaptive prognostic tool will be developed to evaluate engine degradation and determine the remaining useful life for each gas path component. The ultimate goal is a model-driven digital twin for advanced prognostics and capabilities. The project aims to benefit airline and U.S.-based engine and parts manufacturers by contributing to efforts necessary to keep the U.S. commercial aviation engine industry poised to be the world leader in performance, cost, and reliability in the future.

Keywords

• Advanced Analytics, Power Systems, Engine Performance, Optimization Framework, Combustion Monitoring, Preventative Maintenance, Physics-Based Modeling, Engine Degradation, Digital Twin, Prognostics.