Yaguo Lei
Papers
15
Total Citations
425
H-Index
10
About
Yaguo Lei is a prominent researcher specializing in intelligent fault diagnosis, condition monitoring, and the health management of industrial machinery, with a particular focus on industrial robots and their critical components. His work sits at the intersection of machine learning, signal processing, and mechanical engineering, addressing real-world challenges in smart manufacturing and automation. Lei has made significant contributions to transfer learning-based fault diagnosis, developing innovative methods such as distribution barycenter-mediated transfer learning (111 citations) and knowledge-data dual-driven transfer networks (88 citations) that overcome data scarcity and decentralization challenges common in industrial settings. His research on RV reducers—key transmission components in robot manipulators—spans multi-modal signal fusion, nonlinear spectrum analysis, and model-based health monitoring, collectively advancing the reliability of robotic systems. He has also contributed to data quality assurance through graph neural network-based cleaning methods (35 citations), ensuring diagnostic robustness against contaminated datasets. Beyond diagnostics, Lei has engaged with broader questions of construction robotics and robot deflection estimation, demonstrating interdisciplinary reach. His comprehensive review on industrial robot condition monitoring further cements his role as a thought leader shaping research priorities in this rapidly evolving field. His cumulative citation impact reflects growing recognition across both academia and industry.
Research Focus
Key Achievements
Top Papers
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- 3A review for control theory and condition monitoring on construction robots63 citations · 2023
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- 8Condition monitoring and fault diagnosis of industrial robots: A review16 citations · 2024
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- 10Deflection estimation of industrial robots with flexible joints12 citations · 2021