Remaining Useful Life Prediction for Tools Based on Monitoring Data and Stochastic Degradation Model

Abstract

This study proposes a graph convolutional network (GCN)-based data–model interactive remaining useful life (RUL) prediction method for tools. First, a composite health indicator (CHI) is built by aggregating information from neighboring nodes through the GCN. Second, a stochastic degradation model is established to capture the time-varying evolutionary trend. Specifically, the drift coefficient is treated as a random variable to represent its variability among different individuals of the same type of tool, and the model parameters are estimated using intermediate evolutionary process data. Then, a data–model interactive mechanism is proposed by forming closed-loop optimization between the CHI construction and the stochastic degradation model to enhance the RUL prediction accuracy. Finally, experiments are conducted on the PHM2010 dataset to verify the effectiveness and superiority of the proposed method.