Session: 05-06 PHM Systems & Comparative Studies

Paper Number: 129003

129003 - A Comprehensive Literature Review on the Resolution of Turbine Engine Performances’ Inverse Problems 

Turbine engine monitoring is a well-known and well-studied subject that proves to be essential for the aeronautic industry. A popular approach in engine monitoring is constructing indicators that reflect systems’ health states by leveraging operational measurements (i.e., sensors’ data during flights)—this is known as the engine performance’s inverse problem. There exists an extensive literature on this topic, especially revolving around two well-used types of performance indicators of aircraft engines: efficiencies and air mass flow rates of engine’s modules. This review aims to provide a comprehensive survey of this particular literature, which so far has not been properly organized and structured. Our first contribution is to propose a novel taxonomy of the relevant methods. In particular, we consider the role of physics-based models—an element that provides specific advantages and challenges in the context of aircraft engines monitoring—and see if each method exploits such models inside or outside the main algorithmic process (or not exploiting them at all). Our second contribution is to identify the pros and cons of each method, along with additional insights with respect to two commonly encountered challenges: under-determined scenarios and time-series data. Finally, we give some guidelines for selecting appropriate strategies in practical situations and perspectives for future works.

Presenting Author: Sebastien Razakarivony Safran Tech

Presenting Author Biography: I currently hold the position of Tech Lead Engineer at Safran Tech's Digital Sciences and Technologies department. I lead a team consisting of three members, and our primary area of focus is Health Monitoring.
Before joining Safran Tech, I served as a Research Engineer at Safran Electronics and Defense for three years, from 2011 to 2014. During this time, I successfully completed my Ph.D. from Caen University, under the guidance of Dr. Frédéric JURIE. My research primarily revolved around pattern recognition on aerial images.
Prior to pursuing my Ph.D., I graduated from ENSTA Paris in 2011 with an Engineering Diploma. Additionally, I obtained a Master's degree from Université Pierre et Marie Curie.

Authors:

Dong Quan Vu Safran Tech
Sebastien Razakarivony Safran Tech
Yosra Marnissi Safran Tech
Michel Nocture Safran Aircraft Engine