Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit

Remaining Useful Life (RUL) estimation is a fundamental task in the prognostic and health management (PHM) of industrial equipment and systems. To this end, we propose a novel approach for RUL estimation in this paper, based on deep neural architecture due to its great success in sequence learning. Specifically, we take the Transformer encoder as the backbone of our model to capture short- and long-term dependencies in a time sequence. Compared with convolutional neural network based methods, there is no limitation from the kernel size for a complete receptive field of all time steps. While compared with recurrent neural network based methods, we develop our model based on dot-product self-attention, enabling it to fully exploit parallel computation. Moreover, we further propose a gated convolutional unit to facilitate the model's ability of incorporating local contexts at each time step, for the attention mechanism used in the Transformer encoder makes the output high-level features insensitive to local contexts. We conduct experiments on the C-MAPSS datasets and show that, the performance of our model is superior or comparable to those of other existing methods. We also carry out ablation studies and demonstrate the necessity and effectiveness of each component used in the proposed model.

Automated summary

This paper proposes a novel approach for RUL estimation based on deep neural architecture, which effectively utilizes parallel computation and demonstrates superior or comparable performance to other existing methods.