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http://dx.doi.org/10.15207/JKCS.2019.10.12.271

Degradation-Based Remaining Useful Life Analysis for Predictive Maintenance in a Steel Galvanizing Kettle  

Shin, Joon Ho (Department of Convergence Technology & Management Engineering, Yonsei University Graduate School)
Kim, Chang Ouk (Department of Industrial Engineering, Yonsei University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.12, 2019 , pp. 271-280 More about this Journal
Abstract
Smart factory, a critical part of digital transformation, enables data-driven decision making using monitoring, analysis and prediction. Predictive maintenance is a key element of smart factory and the need is increasing. The purpose of this study is to analyze the degradation characteristics of a galvanizing kettle for the steel plating process and to predict the remaining useful life(RUL) for predictive maintenance. Correlation analysis, multiple regression, principal component regression were used for analyzing factors of the process. To identify the trend of degradation, a proposed rolling window was used. It was observed the degradation trend was dependent on environmental temperature as well as production factors. It is expected that the proposed method in this study will be an example to identify the trend of degradation of the facility and enable more consistent predictive maintenance.
Keywords
Predictive Maintenance; Principle Component Regression; Rolling Window; Kettle; Degradation; Remaining Useful Life;
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Times Cited By KSCI : 7  (Citation Analysis)
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