Attention-based Bidirectional LSTM-CNN Model for Remaining Useful Life Estimation

In many industries, prognostic health management (PHM) technology has become important as a key technology to increase reliability and operational efficiency. Recently, several methods using a deep learning architecture to estimate the remaining useful life (RUL) as a part of the PHM have been presented. However, the limitation of existing methods is that they do not explicitly capture the relationship among different time sequences, which reduces the accuracy of RUL estimation. This paper proposes a novel RUL estimation algorithm using the attention mechanism to solve this problem. The proposed method applies scaled dot product attention to the encoder and the decoder consisting of long short-term memory, convolutional neural network and fully connected layer. The encoder applies self-attention to extract the association between time sequences, and the decoder extracts the association between the target RUL value and the time sequences using the representative vector of the RUL. Therefore, the proposed model has better performance to capture the long-term dependency in the sequence data and outperforms other state-of-the-art models in the experimental results. In addition, the extracted attention map shows that our model has better interpretability for RUL estimation.

Automated summary

This paper proposes a novel RUL estimation algorithm using attention mechanism to explicitly capture the relationship among different time sequences, outperforming other state-of-the-art models in experimental results with better interpretability for RUL estimation. The proposed method applies scaled dot product attention to the encoder and decoder, using self-attention in the encoder to extract the association between time sequences and using the representative vector of RUL in the decoder to extract the association between the target RUL value and the time sequences.