Remaining Useful Life Prediction via a Data-Driven Deep Learning Fusion Model-CALAP

Abstract

As one of the key technologies in the field of Prognostic and Health Management (PHM), Remaining Useful Life (RUL) prediction technology plays an important role in equipment health maintenance and fault detection. For complex devices, the degradation process of the remaining useful life of the device is often difficult to be described with mathematical or physical models, so data-driven methods has become an important and feasible method in the field of RUL prediction. This paper proposes a data-driven deep learning fusion model based on CNN-ATTENTION-LSTM-ATTENTION-PARALLEL (CALAP) model, in which Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) are adopted to extract the spatial features and temporal features of the data in parallel, and both of them combine the corresponding attention mechanism allowing the network to focus on important factors. The CNN path fuses CBAM and the LSTM path fuses attention mechanism. We evaluate the proposed model on the C-MAPSS dataset released by NASA and compare it to the state-of-the-art. The RMSE of the proposed model is reduced by nearly 2–5%, and the score is reduced by nearly 8–10% under simple conditions. Experimental results prove that the model has relatively high prediction accuracy and good robustness.

Keywords

• RUL prediction
• convolutional neural networks (CNN)
• convolutional block attention module (CBAM)
• long short-term memory (LSTM)
• attention mechanism
• turbofan engine