Remanufacturing Decision-Making for Gas Insulated Switchgear with Remaining Useful Life Prediction

Seokho Moon, Hansam Cho, Eunji Koh, Yong Sung Cho, Hyoung Lok Oh, Younghoon Kim () and Seoung Bum Kim ()
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Seokho Moon: School of Industrial and Management Engineering, Korea University, 145 Anamro, Seongbuk-gu, Seoul 02841, Korea
Hansam Cho: School of Industrial and Management Engineering, Korea University, 145 Anamro, Seongbuk-gu, Seoul 02841, Korea
Eunji Koh: School of Industrial and Management Engineering, Korea University, 145 Anamro, Seongbuk-gu, Seoul 02841, Korea
Yong Sung Cho: Advanced Power Apparatus Research Center, Korea Electrotechnology Research Institute, 12, Jeongiui-gil, Seongsan-gu, Changwon-si 51543, Korea
Hyoung Lok Oh: WithBeer Co., Ltd., Industry Research Center, 50, Hyeoksinsandan 1-gil, Naju-si 58277, Korea
Younghoon Kim: Department of Industrial and Management Systems Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si 17104, Korea
Seoung Bum Kim: School of Industrial and Management Engineering, Korea University, 145 Anamro, Seongbuk-gu, Seoul 02841, Korea

Sustainability, 2022, vol. 14, issue 19, 1-13

Abstract: Remanufacturing has emerged as a way to solve production problems, as raw material costs increase and environmental pollution caused by discarded equipment occurs. The process can extend product lifetime and prevent waste of resources. In particular, it has economical efficiency for large equipment such as GIS (Gas Insulated Switchgear). The crucial points in remanufacturing are determining replaceable parts and economic valuation. To address these issues, we propose a framework for remanufacturing GIS with remaining lifetime prediction. We construct a regression model for remaining useful life (RUL) in the proposed framework using GIS sensor data. The cost of the replacement parts is estimated with the selected sensors. To validate the effectiveness of the proposed framework, we conducted accelerated life testing on a GIS for data acquisition and applied our framework. The experimental results demonstrate that the tree-based RUL regression model outperforms the others in prediction accuracy. In the simulation of part replacement, the important sensor-based decision-making improves RUL significantly.

Keywords: remanufacturing; gas-insulated switchgear; remaining useful life regression; accelerated life testing; replacement simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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