Bearing Remaining Useful Life Prediction Based on Regression Shapalet and Graph Neural Network

Remaining useful life (RUL) prediction of bearing is essential to guarantee its safe operation. In recent years, deep learning (DL)-based methods attract a lot of research attention for accurate RUL prediction. However, the weak interpretability of the DL models prevents their wide use in practical systems. In this article, the graph is used to represent the degradation state of bearings, and a graph neural network (GNN) is applied for their RUL prediction. Specifically, regression shapelet is proposed to transform the bearings time series data into graph structure first. Then, with the proposed distance matrix/adjacency matrix as the input and smoothed nonlinear health index (SNHI) as the output, a deep GNN model combining graph convolutional neural network (GCN) and gate recurrent unit (GRU) is set up in both spatial and temporal perspectives to predict the bearing RUL. Meanwhile, graph evolution is adopted to monitor the graph changes with time and offer an explanation for the bearing degradation procedure. The experiment study on the PRONOSTIA platform is used to evaluate the proposed method. The results show that the proposed method can well explain the bearing degradation process from the graph perspective and will achieve superior performance to the existing methods.