Session: 05-06 PHM Systems & Comparative Studies

Paper Number: 122290

122290 - Prognostics and Health Management for Electrified Aircraft Propulsion: State of the Art and Challenges 

In recent years, the aviation industry has witnessed a transformative wave of innovation in electrified aircraft propulsion (EAP), driven by sustainability and efficiency goals. Integration of novel electrical subsystems, including high-voltage power electronics, motors/generators, and energy storage devices, has introduced intricate complexities. In this context, an intensified focus on Prognostics and Health Management (PHM) is imperative, considering the heightened reliability needs in a transportation propulsion application. This paper extensively analyzes the current state-of-the-art in PHM applicable to various EAP systems and components crucial for the functioning of electric aircraft. Typical fault modes and fault management strategies are analyzed at various levels of systems hierarchy. An integral aspect of our investigation involves the identification of critical gaps within existing PHM frameworks, guiding the research agenda for enhanced reliability and performance. Moreover, the distributed nature and increasing complexity of electric propulsion systems underscore the importance of Model-Based Systems Engineering (MBSE). We advocate for the exploration of MBSE not only to inform the design and implementation of PHM solutions but also to facilitate certification and Verification and Validation (V&V) activities. Additionally, the paper offers insights into existing tools and simulation software packages capable of integrating traditional gas turbine modules with electric subsystems, as well as simulating various faulty conditions in EAP relevant to PHM development. Key gaps in these tools are emphasized, drawing attention to areas that require further refinement and development. This comprehensive exploration aims to pave the way for future advancements in PHM tailored for the unique challenges posed by electric aircraft propulsion systems.

Presenting Author: Liang Tang GE Research

Presenting Author Biography: Dr. Liang Tang is a Principal Scientist at General Electric Research (GRC) with over twenty years of experience in jet engine controls, diagnostics, prognostics, and AI/Machine Learning applications. At GRC, Liang focuses on applying AI/Machine Learning techniques to engine controls and diagnostics, computer vision-based part inspection, and process automation. Liang has published over sixty papers and book chapters, and holds multiple patents related to engine health management and AI applications. Liang is actively engaged in technical committees and standards organizations such as the SAE, ASME and PHM society.

Authors:

Liang Tang GE Research
Abhinav Saxena GE Research
Karim Younsi GE Research