An interpretable RUL prediction method of aircraft engines under complex operating conditions using spatio-temporal features

Long short-term memory (LSTM) based prediction methods have achieved remarkable achievements in remaining useful life (RUL) prediction for aircraft engines. However, their prediction performance and interpretability are unsatisfactory under complex operating conditions. For aircraft engines with high hazard levels, it is important to ensure the interpretability of the models while maintaining excellent prediction accuracy. To address these issues, an interpretable RUL prediction method of aircraft engines under complex operating conditions using spatio-temporal features (STFs), referred to as iSTLSTM, is proposed in this paper. First, we develop a feature extraction framework called Bi-ConvLSTM1D. This framework can effectively capture the spatial and temporal dependencies of sensor measurements, significantly enhancing the feature extraction capabilities of LSTM. Then, an interpretation module for STFs based on a hybrid attention mechanism is designed to quantitatively assess the contribution of STFs and output interpretable RUL predictions. The effectiveness of iSTLSTM is evidenced by extensive experiments on the C-MAPSS and N-CMAPSS datasets, confirming the superiority and reliability of our method for aircraft engine RUL prediction.