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

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 29; no. 3; pp. 668 - 676
• Main Authors: Zhang, Baokang, Li, Ning, Huang, Jiahui, Arakawa, Takahiro, Ishii, Kentaro, Yashima, Ryuichi
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 20.05.2025
• Subjects: Accuracy; Artificial neural networks; Closed loops; Crack propagation; Degradation; Life prediction; Manufacturing; Mathematical functions; Methods; Neural networks; Parameter estimation; Random variables; Sensors; Stochastic models; Useful life

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 i...