Remaining Useful Life Prediction Based on CNN-BGRU-SA

Abstract

Remaining Useful Life (RUL) prediction has very high importance for improving the safety and reliability of equipment, and its accurate estimation can provide technical support for fault Prognostics and Health Management (PHM). A hybrid deep neural network model has been proposed to improve the prognosis accuracy of equipment RUL. Convolution Neural Network (CNN) is used to extract the local features with Bidirectional Gated Recurrent Unit (BGRU) capturing the anterior and backward long-term dependence and then Self Attention (SA) assigning weights. Finally, Fully-Connection Network (FCN) is stacked to output the RUL prediction value, and its superiority is verified on C-MAPSS dataset. Experimental results show that CNN-BGRU-SA acquires better prognosis performance compared with a single network and the approach based on the BLCNN model, with high accuracy and generalization.