Yicheng Jin

Agency for Science, Technology and Research

Papers

Total Citations

H-Index

About

Yicheng Jin is a researcher specializing in predictive maintenance, condition monitoring, and smart manufacturing, with a focus on integrating advanced data-driven techniques into industrial systems. His most-cited work, "Condition Monitoring for Predictive Maintenance of Machines and Processes in ARTC Model Factory" (2021), has garnered 5 citations and exemplifies his contributions to developing real-time monitoring frameworks that enhance operational efficiency and reduce downtime in manufacturing environments. Jin’s research bridges the gap between theoretical modeling and practical implementation, leveraging sensor data and machine learning to predict equipment failures before they occur. His work within the ARTC Model Factory highlights his commitment to translating academic insights into tangible industry solutions, making him a key figure in advancing Industry 4.0 practices. By addressing critical challenges in maintenance optimization, Jin’s studies offer valuable methodologies for engineers and researchers aiming to improve production reliability and cost-effectiveness. His growing citation record reflects the relevance of his findings, positioning him as an emerging voice in the field of industrial automation and predictive analytics.