Remaining Useful Life Prediction of Turbofan Engine using Long-Short Term Memory

The aero-engine is a crucial component of the aircraft that provides thrust for the plane. To ensure the safety of the aircraft, it is vital to estimate the remaining useful life (RUL) of the engine. Over the past decades, research regarding Prognostic Health Management (PHM) has gained popularity in the field of engineering due to the machineries' fault. The failure of the machinery systems can cause many incidents, such as delays or an increase in operating costs. Thus, to monitor the reliability and safety of an engineering system, which improves the maximum operating availability and reduces maintenance cost, RUL is used to predict the future performance of the machinery to prevent fault. This study proposes a model for RUL estimation based on Long-Short Term Memory (LSTM), which can fully exploit sensor sequence information and reveal hidden patterns in sensor data. The proposed LSTM model has achieved an accuracy of 0.978 and F1-score of 0.960. While the regression model performance has been evaluated using three evaluation matric, mean absolute error (MAE), coefficient of determination (R2), recall. Lastly, the results achieved for MAE, R2 and recall were 12, 0.7856 and 1, respectively.