The turbofan engine is a pivotal component of the aircraft. Engine components are susceptible to degradation over the life of their operation, which affects the reliability and performance of an engine. In order to direct the necessary maintenance behavior, remaining useful life prediction is the key. This research uses machine learning to provide a prediction framework for an aircraft’s remaining useful life (RUL) based on the entire life cycle data and deterioration parameter data (ML). For the engine’s lifetime assessment, a Deep Layer Recurrent Neural Network (DL-RNN) model is presented. The suggested method is compared to Multilayer Perceptron (MLP), Nonlinear Auto Regressive Network with Exogenous Inputs (NARX), and Cascade Forward Neural Network (CFNN), as well as the Prognostics and Health Management (PHM) conference Challenge dataset and NASA’s C-MAPSS dataset. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are calculated for both the datasets, and the values are in the range of 0.15% to 0.203% for DL-RNN, whereas for the other three topologies, they are in the range of 0.2% to 4.8%. Comparative results show a better predictive accuracy with respect to other ML algorithms.

Turbofan engine is a pivotal component of the aircraft. Engine components are susceptible to degradation over the life of their operation which affects the reliability and performance of an engine. In order to direct the necessary maintenance behavior, remaining useful life prediction is the key. This thesis presents a prediction framework for the Remaining Useful Life (RUL) of an aircraft engine using the whole life cycle data and deterioration parameter data based on a machine learning (ML) approach. In specific, a Deep Layer Recurrent Neural Network (DL-RNN) model is proposed to address the problem of prognostic instability based on deep learning. In addition, for the aircraft engine, a new health indicator (HI) measure is implemented based on the preprocessing of raw data. The proposed method is compared against Multilayer-Perceptron (MLP), Non-linear Auto Regressive Network with Exogenous Inputs (NARX), Cascade Forward Neural Network (CFNN) and validated through the IEEE 2008 Prognostics and Health Management (PHM) conference Challenge dataset and Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset provided by NASA results reveal a better predictive precision with respect to other ML algorithms.