A deep learning-based prognostic framework for aeroengine exhaust gas temperature margin
Weigang Fu1, Xiang Tan2, Liangzhong Ao1, Yaoming Fu1, Peng Guo3,4
Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.39, No.2, pp. 1-9, 2023, DOI:10.23967/j.rimni.2023.05.002
- 16 May 2023
Abstract The value of the gas-path parameter, exhaust gas temperature margin (EGTM), is the critical index for predicting aeroengine performance degradation. Accurate predictions help to improve engine maintenance, replacement schedules, and flight safety. The outside air temperature (OAT), altitude of the airport, the number of flight cycles, and water washing information were chosen as the sample input variables for the data-driven prognostic model for predicting the take-off EGTM of the on-wing engine. An attention-based deep learning framework was proposed for the aeroengine performance prediction model. Specifically, the multiscale convolutional neural network (CNN) structure is designed to More >