Remaining useful life prediction of turbofan engines based on dual attention mechanism guided parallel CNN-LSTM

Abstract

Abstract Remaining useful life (RUL) prediction is an valuable research task for predictive health management of aero engines, which is crucial to enhance the safety, dependability and economy of the motor. Accurate prediction of remaining engine service life is an important means for the effectively monitoring of engine operating conditions. The forecasting accuracy of turbofan RUL is inadequate with the traditional single-parameter, single-stage mode. To boost the precision of aero-engine RUL anticipation, a novel mode is recommended, based on a parallel convolutional neural network (CNN) with a long and short-term memory neural network (LSTM) and a dual attention mechanism, named PCLD. The degradation information directly from time series sensor data. The advantages of convolutional neural network (CNN) networks and long short-term memory (LSTM) networks in feature mining and time series processing, respectively, are employed to process time series data, which is conducive to preventing the loss of important element in the data. At last, the experimental results on the aero-engine performance recession dataset C-MAPSS demonstrate that the method outperforms the currently popular models with better robustness and higher prediction accuracy.