Papers
1
Total Citations
16
H-Index
1
About
Dr. Xinqi Shen is a leading researcher in the fields of digital twin technology, predictive maintenance, and intelligent manufacturing systems. Their most impactful work centers on bridging the gap between physical and virtual data to enhance the reliability of industrial machinery. In their highly cited 2025 paper, Shen introduced a groundbreaking digital twin-driven framework that integrates a water-wave information transmission mechanism with a recurrent acceleration network, specifically designed to predict the remaining useful life (RUL) of gearboxes. This innovation directly addresses the critical limitation of traditional methods—their failure to correlate physical and virtual world data—which often results in low prediction accuracy and operational disruptions in robotic systems. With 16 citations already, this work demonstrates significant early impact in the field. Shen’s contributions are pivotal for advancing smart manufacturing and Industry 4.0, offering a robust solution for real-time equipment health monitoring and failure prevention. Their research continues to shape the future of predictive analytics in complex mechanical systems.
Research Focus
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Top Papers
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