Papers
6
Total Citations
78
H-Index
4
About
Jianying Li is a leading researcher in intelligent fault diagnosis and robotic control systems, with a particular focus on enhancing the reliability and performance of industrial and medical robots. Their most impactful work addresses the critical challenge of diagnosing faults in harmonic drives—the core components of industrial robots—where scarce fault data often hinders traditional machine learning methods. Li pioneered the use of Generative Adversarial Networks (GANs) to handle imbalanced data, a breakthrough that has garnered 47 citations and set a new standard for fault diagnosis in robotics. Complementing this, their convolutional neural network-based method for compound fault diagnosis further advances the field, earning 8 citations. Beyond fault detection, Li has contributed to stereo visual-inertial localization for orchard robots, compliant control of lower limb exoskeletons, and even force-feedback telesurgery simulators, showcasing a versatile expertise spanning agricultural robotics, rehabilitation technology, and medical applications. With over 78 total citations across their most-cited works, Jianying Li’s research continues to drive innovation in making robots more robust, autonomous, and safe for real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Simple Global Thresholding Neural Network for Shadow Detection2 citations · 2021
- 6A Master-Slave Telesurgery Simulator with Force-Feedback2 citations · 2012