Res-HSA: Residual hybrid network with self-attention mechanism for RUL prediction of rotating machinery

Abstract

Remaining useful life (RUL) prediction is of great significance for improving maintenance efficiency and ensuring the reliability of rotating machinery. In recent years, there are a large number of deep-learning-based methods for RUL prediction of rotating machinery. However, the effect of conventional end-to-end RUL prediction methods relies on the distribution consistency of training data and test data, and conventional health indicator extrapolation RUL prediction methods are susceptible to interference from abnormal fluctuations in the health curve. To overcome the problem, this paper proposes a new RUL prediction method for rotating machinery using health indicators constructed by the residual hybrid network with self-attention mechanism (Res-HSA). First of all, we propose the residual hybrid network combined with self-attention mechanism to extract the high-level degenerate feature. Then, the health assessment model based on Res-HSA is proposed to generate the health indicators of the equipment. To assist in network training, the segmented data labels based on the degradation rule are applied to optimize the labels of training sets. Finally, to address the problem of abnormal fluctuations in the health curve, a fitting interval selection method is used to optimize conventional curve fitting schemes to calculate RUL. Two public datasets, IEEE-PHM-2012-challenge datasets and C-MAPSS datasets, are used to verify the effectiveness of the proposed method. The experiment results on two public datasets show that the RUL prediction method proposed in this paper has good prediction performance. Compared to the state-of-the-art method, the method proposed in this article reaches the most advanced level in some test projects, while the rest of the projects can be very close to the most advanced method.