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

• Published in: Sustainability Vol. 14; no. 19; p. 12357
• Main Authors: Moon, Seokho, Cho, Hansam, Koh, Eunji, Cho, Yong Sung, Oh, Hyoung Lok, Kim, Younghoon, Kim, Seoung Bum
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Accelerated life tests; Algorithms; Data acquisition; Data entry; Decision making; Failure; Fault diagnosis; Gases; Laboratories; Lasers; Life prediction; Machine learning; Power supply; Predictions; Raw materials; Remanufacturing; Sensors; Simulation; Sustainability; Switchgear; Useful life; South Korea
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su141912357

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.