Estimation of Exhaust Gas Temperature Using Artificial Neural Network in Turbofan Engines

This paper deals with the estimation of exhaust gas temperature (EGT) of a CFM56-7B turbofan engine using artificial neural network (ANN) at two different power settings, maximum continuous and take-off. The study was carried out using the operational parameters of the engine such as net thrust, fuel flow, low rotational speed, core rotational speed, pressure ratio, fan air inlet temperature, take-off margin temperature, and thrust specific fuel consumption. All these data are taken from test cell measurements during ground operating of the engines. In this study, the accuracy of ANN results is compared with the measurements and the results of a regression analysis earlier based multiple linear method. The comparison of the predictions of the models indicates that ANN is capable of accurately predicting EGT in used turbofan engines. The correlation between the exhaust gas temperature and the operational parameters of the engine was found to be 0.99 and 0.99 for training data and to be 0.90 and 0.97 for test data using ANN at two different power settings, maximum continuous and take-off, respectively. For both investigated power settings, maximum continuous and take-off, the mean absolute errors were found to be 2.1 per cent and 5.08 per cent, while the coefficients of variance of root mean square error were found to be 0.5705 and 0.3539, respectively. The results obtained from ANN models show good agreement with ground measurements and the regression models. Finally, we believe that ANN can be used for prediction of EGT as a predictive tool in this sort of application.