Sustainable Aviation: A Review in Intelligent Health Management for Decarbonized Aerospace Propulsion

Considering the challenges posed by environmental concerns, hydrogen and electrification are emerging as promising pathways towards the decarbonization of the aerospace industry. The incorporation of electrical systems and multiple energy sources on board has increased the complexity of aero-space propulsion systems. This has led to greater demands on health management, which aims to enhance the maintainability, reliability, cost-effectiveness, safety, and performance of novel propulsion systems. Lever-aging reasoning technologies, it can maximize the timeliness and reliability of diagnostic and prognostic information, where commonly used techniques include model-based and data-driven approaches chosen based on specific applications and systems. This paper examines the advantages and disadvantages of widely used model-based (Kalman filter, particle filter) and data-driven algorithms (artificial neural network, support vector machine) supported by recent applications and research in aerospace propulsion. These examples justify the effectiveness of health management within specific system configurations and operational scenarios, concluding with a brief of challenges and opportunities for intelligent health management targeting the decarbonization of aerospace propulsion systems.