Xiaoqin Liu
Papers
18
Total Citations
176
H-Index
9
About
Xiaoqin Liu is a prominent researcher specializing in condition monitoring, fault diagnosis, and electromechanical systems for industrial robots, with a particular focus on key drivetrain components such as RV reducers and joint servo transmission systems. Their work bridges mechanical dynamics and intelligent signal processing, contributing foundational advances in how robotic systems are monitored and maintained throughout their operational lifespan. Liu's most influential contribution — garnering 41 citations — introduces an electromechanical coupling model enabling motor current signature analysis for detecting bolt loosening in robot joints, a novel approach that eliminates the need for additional sensors. Complementary work explores vibration prediction using elastic joint dynamics (15 citations), encoder-based transient feature extraction, and acoustic emission-based condition monitoring dating back to 2016 (18 citations), demonstrating a sustained and evolving research trajectory. Liu has also advanced intelligent fault diagnosis through knowledge distillation-based network compression (19 citations) and remaining useful life prediction combining deep learning with multicore support vector methods. A recurring theme across Liu's portfolio is extracting meaningful diagnostic information from unconventional or bandwidth-limited signals, as evidenced by research on non-uniform sampling reconstruction and instantaneous angular speed estimation. Collectively, Liu's work has accumulated over 150 citations, establishing them as a significant voice in robot health monitoring and predictive maintenance research.
Research Focus
Key Achievements
Top Papers
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- 7Tooth root crack detection of planet gear in industrial robot RV reducer10 citations · 2023
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