Work

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

Year: 2025

Type: article

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.

Source: Journal of Advanced Computational Intelligence and Intelligent Informatics

DOI: 10.20965/jaciii.2025.p0668

Authors: Baokang Zhang, Ning Li, Jiahui Huang, Takahiro Arakawa, Kentaro Ishii, Ryuichi Yashima

Institutions: Zhejiang University of Technology, Tokyo University of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funder: Japan Society for the Promotion of Science

Award: Highly Sensitive Imaging system of Biological Components on Skin Surface for Evaluation of Wound Healing