Version 1
: Received: 27 July 2022 / Approved: 2 August 2022 / Online: 2 August 2022 (09:20:14 CEST)
Version 2
: Received: 11 September 2022 / Approved: 12 September 2022 / Online: 12 September 2022 (10:12:33 CEST)
Menga, N.; Mothakani, A.; Giorgi, M.G.D.; Przysowa, R.; Ficarella, A. Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine. Energies2022, 15, 7304.
Menga, N.; Mothakani, A.; Giorgi, M.G.D.; Przysowa, R.; Ficarella, A. Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine. Energies 2022, 15, 7304.
Menga, N.; Mothakani, A.; Giorgi, M.G.D.; Przysowa, R.; Ficarella, A. Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine. Energies2022, 15, 7304.
Menga, N.; Mothakani, A.; Giorgi, M.G.D.; Przysowa, R.; Ficarella, A. Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine. Energies 2022, 15, 7304.
Abstract
Micro turbojets are used for propelling radio-controlled aircraft, aerial targets and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional Artificial Neural Network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and Exhaust Gas Temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.
Keywords
ELM; ANN; compressor; turbine; degradation; microturbine; engine health management
Subject
Engineering, Energy and Fuel Technology
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: Radoslaw Przysowa
Commenter's Conflict of Interests: Author