Distributed Attention-Based Temporal Convolutional Network for Remaining Useful Life Prediction

Abstract

Massive industrial data collected from the Industrial Internet-of-Things (IIoT) assets improve data-driven methods for prognostics and health management (PHM) systems. As an important role in PHM, remaining useful life (RUL) prediction is essential to maintain the reliability and safety of industrial manufacture. However, recent data-driven approaches for bearing RUL prediction do not weight the contributions of data from different sensors and time steps, which decreases the efficiency in the big data era. In this context, we present a deep learning-based RUL prediction method with an attention mechanism to weight sequence data representations. Specifically, the proposed method weights different industrial sensors and time steps, respectively, based on the distributed attention mechanism. Then, temporal convolution modules with the shared weights are used for feature extraction of time series. Finally, the performance of the proposed method is verified using a popular data set C-MAPSS. The experimental results reveal that the proposed method has high accuracy and is efficient in practical applications.