Long-term temporal attention neural network with adaptive stage division for remaining useful life prediction of rolling bearings

Abstract

Accurate rolling bearing remaining useful life (RUL) prediction, an effective assurance of the rotating machinery's safety and reliability, is one of the essential procedures in equipment maintenance. Current RUL prediction methods mostly adopt direct prediction methods, but it is difficult for them to guarantee prediction accuracy under the influence of longlife cycles and variable production environments. Therefore, a long-term temporal attention neural network with adaptive stage division (AD-LTAN) is proposed to predict the RUL of rolling bearings. Aiming at the large fluctuation range of the degradation starting point, an adaptive stage division model with the augmentation of early features is proposed to analyze the long-sequence signals, and then the life cycle of the bearings will be divided into different health stages. Aiming at the network memory decline under the longlife cycle of degradation, a long-term temporal attention neural network is designed to retain the long-term degradation characteristics of bearings by leveraging multilevel expansion convolution and integrating attention mechanisms to extract the fault signal features to realize the RUL prediction. The experimental results conducted on the PHM2012 and XJTU-SY datasets demonstrate that the proposed method outperforms the compared methods in terms of prediction loss (35.4 % less than their best).