Author: Martin Gawecki and C.-C. Jay Kuo
This work is part funded by the Pratt &Whitney Institute for Collaborative Engineering, in collaboration with Pratt & Whitney, Korean Airlines, and INHA University.

As a supplement to previous work on vibration and acoustic approaches [1], Gas Path Analysis (GPA) is the study of a gas-turbine jet engine’s temperature, pressure, and rotational parameters with the aim of detecting and diagnosing problems that may occur during flight (see Figure 1), commonly known as the field of Prognostics and Health Management (PHM). General applications of GPA for steady-state engine operation have been used successfully for over two decades in a limited capacity, due to the technological restrictions of on-board computing, costly transmission capabilities, and a lack of comprehensive analysis tools. While this foundational problem is slowly being addressed, even fewer answers exist to the question of whether GPA can be used for the immediate recognition and identification of faults that may occur during engine transients (non-steady-state regions of operation, shown in gray in Figure 2). Our research aims to explore this problem in order to build real-time tools that improve the maintenance workflow and to help develop the next generation of “intelligent engines.”

Using a variety of data created using the state-of-the-art Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) [2, 3, 5], we propose a contextualized combination of state variable and empirical models for the purposes of feature extraction [4], coupled with a machine learning framework for the detection and identification of faults. In essence, we hope to use existing normal (non-fault) signatures of GPA parameters at steady-state as a reference for transient points of operation, in the hopes of finding and classifying abnormal behavior. Figure 3 outlines the feature extraction process [...]