Quanbo Lu
Papers
2
Total Citations
26
H-Index
2
About
Quanbo Lu is a leading researcher in intelligent fault diagnosis and predictive maintenance for industrial robotics, with a focus on integrating digital twin technology and deep learning to enhance machinery reliability. His work centers on developing advanced methods for remaining useful life (RUL) prediction and fault detection in critical components like gearboxes and rolling element bearings. Lu’s major contributions include pioneering a digital twin-driven water-wave information transmission and recurrent acceleration network, which bridges physical and virtual world data to significantly improve RUL prediction accuracy for gearboxes—a key challenge in preventing robot malfunctions. He also introduced a novel fault prediction method for rolling element bearings that combines digital twins with deep transfer learning, overcoming the limitations of traditional approaches that require identical training and testing data distributions. With his most-cited papers accumulating over 26 citations since 2025, Lu’s research directly addresses costly system downtime and repair expenses in industrial settings. His notable achievements include advancing the practical application of digital twins in real-time monitoring, offering robust, data-efficient solutions that reduce economic losses and enhance operational safety in automated manufacturing environments.
Research Focus
Key Achievements
Top Papers
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- 2