A Temporal Self-Attention Guided Deep Learning Approach for Prognosis of Remaining Useful Life of Aircraft Turbofan Engines

In the aviation industry, a reliable prognosis of the remaining useful life (RUL) of aircraft engines is a prerequisite for effective predictive maintenance as well as averting critical failures. But the high non-linearity of engine components and operation in complex environments make it more challenging to capture the deterioration behavior and estimate the RUL. To deal with this concern, a deep learning ensemble model with a Temporal Self-Attention Mechanism (TSAM) is presented to estimate the RUL of aircraft turbofan engines effectively. Firstly, the importance of different sensors is evaluated by applying the TSAM method to the raw sensor data. Then Temporal Convolutional Network (TCN) is applied to the weighted sensor data to extract high-dimensional features. Next, a Bi-directional LSTM network…