Hansam Cho1, Seokho Moon1, Sunhyeok Hwang1, Seoung Bum Kim1,*, Younghoon Kim2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1941-1967, 2025, DOI:10.32604/cmes.2025.072455
- 26 November 2025
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 More >