@Article{cmes.2025.072455,
AUTHOR = {Hansam Cho, Seokho Moon, Sunhyeok Hwang, Seoung Bum Kim, Younghoon Kim},
TITLE = {Data-Driven Component-Level Decision-Making for Online Remanufacturing of Gas-Insulated Switchgear},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {2},
PAGES = {1941--1967},
URL = {http://www.techscience.com/CMES/v145n2/64585},
ISSN = {1526-1506},
ABSTRACT = {Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment. However, existing approaches face three significant limitations: (1) reliance on predefined mathematical models that often fail to capture equipment-specific degradation, (2) offline optimization methods that assume access to future data, and (3) the absence of component-level guidance. To address these challenges, we propose a data-driven framework for component-level decision-making. The framework leverages streaming sensor data to predict the remaining useful life (RUL) without relying on mathematical models, employs an online optimization algorithm suitable for practical settings, and, through remanufacturing simulations, provides guidance on which components should be replaced. In a case study on gas-insulated switchgear, the proposed framework achieved RUL prediction performance comparable to an oracle model in an online setting without relying on predefined mathematical models. Furthermore, by employing online optimization, it determined a remanufacturing timing close to the global optimum using only past and current data. In addition, unlike previous studies, the framework enables component-level decision-making, allowing for more detailed and actionable remanufacturing guidance in practical applications.},
DOI = {10.32604/cmes.2025.072455}
}