Papers
15
Total Citations
268
H-Index
11
About
Jun Wu is a multidisciplinary researcher whose work spans rehabilitation robotics, intelligent fault detection, and predictive maintenance for mechanical systems. Early in his career, Wu made significant contributions to assistive technology, developing wearable rehabilitation robotic hands driven by Pneumatic Muscle–Torsion Spring (PM-TS) actuators to support stroke and traumatic brain injury survivors in recovering hand motor function. His pioneering fuzzy PID and RLSESN-based adaptive control strategies addressed the complex nonlinear dynamics of pneumatic muscles, earning over 90 combined citations across foundational papers from 2009 to 2012. Wu also contributed to upper-limb exoskeleton development, designing intention-driven control approaches for patients with limited mobility. In recent years, Wu has pivoted toward industrial intelligence, producing highly impactful work in zero-fault-sample fault detection and remaining useful life (RUL) prediction. His residual shrinkage transformer relation network and degradation-aware transformer frameworks represent cutting-edge applications of deep learning to industrial robot health monitoring. His 2023 fault detection paper has already accumulated 32 citations, reflecting rapid community uptake. Across both phases of his career, Wu demonstrates a consistent commitment to bridging advanced control theory and machine learning with real-world engineering challenges, making his work highly relevant to researchers in robotics, smart manufacturing, and healthcare technology.
Research Focus
Key Achievements
Top Papers
- 1A Wearable Rehabilitation Robotic Hand Driven by PM-TS Actuators40 citations · 2010
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