A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems

Abstract

Intelligent Control and Health Management technology for aircraft propulsion systems is much more developed in the laboratory than in practice. With a renewed emphasis on reducing engine life cycle costs, improving fuel efficiency, increasing durability and life, etc., driven by various government programs, there is a strong push to move these technologies out of the laboratory and onto the engine. This paper describes the existing state of engine control and on-board health management, and surveys some specific technologies under development that will enable an aircraft propulsion system to operate in an intelligent way—defined as self-diagnostic, self-prognostic, self-optimizing, and mission adaptable. These technologies offer the potential for creating extremely safe, highly reliable systems. The technologies will help to enable a level of performance that far exceeds that of today’s propulsion systems in terms of reduction of harmful emissions, maximization of fuel efficiency, and minimization of noise, while improving system affordability and safety. Technologies that are discussed include various aspects of propulsion control, diagnostics, prognostics, and their integration. The paper focuses on the improvements that can be achieved through innovative software and algorithms. It concentrates on those areas that do not require significant advances in sensors and actuators to make them achievable, while acknowledging the additional benefit that can be realized when those technologies become available. The paper also discusses issues associated with the introduction of some of the technologies.

DOI: 10.2514/1.13048

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Cite this paper

@article{Litt2004ASO, title={A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems}, author={Jonathan S. Litt and Donald L. Simon and Sanjay Garg and Ten-Huei Guo and Carolyn Mercer and Richard Millar and Alireza Behbahani and Anupa Bajwa and Daniel T. Jensen}, journal={JACIC}, year={2004}, volume={1}, pages={543-563} }