アラカワ　タカヒロ   荒川　貴博    所属   工学部 電気電子工学科    職種   教授
言語種別	英語
発行・発表の年月	2025/05
形態種別	学術論文
査読	査読あり
標題	Remaining Useful Life Prediction for Tools Based on Monitoring Data and Stochastic Degradation Model
執筆形態	共著
掲載誌名	Journal of Advanced Computational Intelligence and Intelligent Informatics
掲載区分	国外
出版社・発行元	FUJI TECHNOLOGY PRESS LTD
巻・号・頁	29(3),pp.668-676
総ページ数	9
担当区分	責任著者
国際共著	国際共著
著者・共著者	Zhang B, Li N, Huang J, Arakawa T, Ishii K, Yashima R
概要	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.
外部リンクURL	https://doi.org/10.20965/jaciii.2025.p0668