Aircraft engine health management via stochastic modelling of flight data interrelations |
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| Authors: | D. Dimogianopoulos J. Hios S. Fassois |
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| Affiliation: | 1. Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, United States;2. ChemPlant Technology, 400 01, Czech Republic;1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology), Ministry of Education, China;2. China Gas Turbine Establishment, Aero Engine Corporation of China, Chengdu, 610500, China;3. School of Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;1. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China;2. Department of Mechanical and Materials Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada;3. Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada |
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| Abstract: | A novel engine health management (EHM) scheme is introduced. It uses flight-level, instead of thermodynamic, data to cost-effectively augment the onboard EHM redundancy. For a nominal healthy aircraft, fault-sensitive interrelations among flight data are globally modelled inside a flight regime via Constant-Coefficient Pooled Nonlinear AutoRegressive with eXogenous (CCP-NARX) excitation representations. Single or sequential engine faults perturb these interrelations. Statistically evaluating the perturbation-induced effects draws reliable conclusions on the engine?s health. Validation and comparisons with Kalman filter-based alternatives are made throughout the regime under various operational conditions. |
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