Changqing Shen
Soochow University, University of Science and Technology of China
Papers
5
Total Citations
65
H-Index
4
About
Changqing Shen is a leading researcher in intelligent fault diagnosis and condition monitoring for industrial robotics, with a particular focus on the reliability of critical components such as ball screws and harmonic reducers. His work addresses the pressing challenge of diagnosing faults in complex, variable, and inaccessible working conditions—a key bottleneck for modern automated manufacturing. Shen’s major contributions include pioneering the use of non-vibration signals for fault diagnosis in industrial robots, as demonstrated in his highly cited 2024 paper (22 citations), and developing advanced dynamics models for flexible thin-walled elliptical bearings (20 citations). He is also at the forefront of domain adaptation and few-shot learning, proposing innovative methods like Metric Learning-Based Few-Shot Adversarial Domain Adaptation for cross-machine diagnosis, which overcomes data distribution differences across robots. His joint domain adaptation approach further enables lightweight, cross-device, and multimodal sensing compatibility. With over 65 total citations in 2024 alone, Shen’s work is rapidly gaining recognition for its practical impact on smart manufacturing. Earlier in his career, he also contributed to service robotics, designing a beverage-sale robot, showcasing his broad engineering expertise.
Research Focus
Key Achievements
Top Papers
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- 5Design of a Beverage-Sale Service Robot2 citations · 2014