Version 1
: Received: 6 July 2023 / Approved: 6 July 2023 / Online: 7 July 2023 (09:03:47 CEST)
Version 2
: Received: 8 July 2023 / Approved: 10 July 2023 / Online: 11 July 2023 (08:22:53 CEST)
Rodrigo, J.; Sanchez de Leon, L.; Montañes, J.L.; Vega, J.M. A Reduced Order Model for Monitoring Aeroengines Condition in Real Time. Aerospace2023, 10, 861.
Rodrigo, J.; Sanchez de Leon, L.; Montañes, J.L.; Vega, J.M. A Reduced Order Model for Monitoring Aeroengines Condition in Real Time. Aerospace 2023, 10, 861.
Rodrigo, J.; Sanchez de Leon, L.; Montañes, J.L.; Vega, J.M. A Reduced Order Model for Monitoring Aeroengines Condition in Real Time. Aerospace2023, 10, 861.
Rodrigo, J.; Sanchez de Leon, L.; Montañes, J.L.; Vega, J.M. A Reduced Order Model for Monitoring Aeroengines Condition in Real Time. Aerospace 2023, 10, 861.
Abstract
A reduced order model is developed to monitor aeroengines condition (defining their degradation from a baseline state) in real-time, by using data collected in specific sensors. This reduced model is constructed by applying higher order singular value decomposition plus interpolation to appropriate data, organized in tensor form. Such data are obtained using a detailed engine model that takes the engine physics into account. Thus, the method synergically combines the advantages of data-driven (fast online operation) and model-based (the engine physics is accounted for) condition monitoring methods. Using this reduced order model as surrogate of the engine model, two gradient-like condition monitoring tools are constructed. The first tool is extremely fast and able to precisely compute `on the fly’ the turbine inlet temperature, which is a paramount parameter for the engine performance, operation, and maintenance, and can only be roughly estimated by the engine instrumentation in civil aviation. The second tool is not so fast (but still reasonably inexpensive) and precisely computes both, the engine degradation and the turbine inlet temperature at which sensors data have been acquired. These tools are robust in connection with random noise added to the sensors data and can be straight forwardly applied to other mechanical systems.
Keywords
Reduced order models; Higher order singular value decomposition; Health monitoring; Aeroengines; Predictive maintenance; Degradation parameters; Sensors scaling; Turbine inlet temperature; Gradient-like methods; Noisy data
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: José M Vega
Commenter's Conflict of Interests: Author