Turbofan engine health status prediction with artificial neural network

The main purpose of this study is to present the concept of the aircraft turbofan engine health status prediction with artificial neural network augmentation process. The main idea of engine health status prediction is based on the engine health status parameter broadly used in the aviation industry as well as propulsion technology being the performance and safety margin. As a result of research engine health status index is calculated in order to determine the engine degradation level. The calculated parameter is then used as a response parameter for the machine learning algorithm. The case study is based on the artificial neural network which was two-layer feedforward network with sigmoid hidden neurons and linear output neurons. Network performance is evaluated using mean squared error and regression analysis. The final results are analyzed using visualization plots such as regression fit plot and histogram of errors. The greatest achievement of this elaboration is the presentation of how the entire process of engine status prediction might be augmented with the use of an artificial neural network. What is the greatest scientific contribution of the article is the fact that there are no scientific studies available, which are based on the engine real-life operating data.