Papers
3
Total Citations
11
H-Index
2
About
Yaqiao Zhu’s research focuses on the intersection of industrial robotics, electromechanical systems, and intelligent condition monitoring. His key contributions lie in developing advanced methods for fault diagnosis, remaining useful life prediction, and parameter identification in robotic systems. Notably, his 2023 work on the harmonic reducer—a critical component in industrial robots—introduced novel prognostic features by fusing vibration and current signals to improve failure analysis and life prediction accuracy, addressing the challenge of false alarms in complex working environments. This paper has garnered 6 citations, reflecting its growing relevance in predictive maintenance. Zhu also pioneered the Particle Gray Wolf Optimization Algorithm (PSOGWO) for optimizing excitation trajectories in inertial parameter identification, a step-by-step approach that enhances robot modeling precision. Earlier, he applied machine vision to automate roundness detection and sorting of oil cooling pipes, solving real-time quality control issues in manufacturing. Through these contributions, Zhu demonstrates a commitment to bridging signal processing, optimization, and practical industrial applications, making his work valuable for researchers and engineers advancing robotic reliability and smart manufacturing.
Research Focus
Key Achievements
Top Papers
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