Prof. Dr. Weidong Jiao, Zhejiang Normal University, China
Dr. Weidong Jiao was born in Wafangdian, Liaoning, China, in 1970. He received his B.E. and M.E. degrees in Safety Engineering and Mechanical Engineering from Gansu University of Technology in 1992 and 2001, respectively, and earned his Ph.D. in Mechanical Engineering from Zhejiang University in 2004. From 2004 to 2009, he served as a Professor in the Mechanical Engineering Department at Jiaxing University. Since 2013, he has been a Professor at the School of Engineering, Zhejiang Normal University. Dr. Jiao has authored over 100 research articles and holds approximately 20 invention patents. His research focuses on smart testing and signal processing, mechanical dynamics, and condition monitoring and fault diagnosis of mechanical equipment. He also serves as an Editor for the Journal of Vibration, Measurement & Diagnosis.
SCOPUS
Summary of Suitability for Best Researcher Award – Prof. Weidong Jiao
Prof. Weidong Jiao is a highly qualified candidate for the Best Researcher Award, based on his extensive contributions to mechanical engineering, fault diagnosis, and intelligent signal processing. His strong research background, innovative work, and leadership in academia make him a worthy contender for this prestigious recognition.
🎓 Education:
- B.E. in Safety Engineering – Gansu University of Technology, Lanzhou (1992)
- M.E. in Mechanical Engineering – Gansu University of Technology, Lanzhou (2001)
- Ph.D. in Mechanical Engineering – Zhejiang University, Hangzhou (2004)
💼 Work Experience:
- Professor, Mechanical Engineering Department, Jiaxing University (2004–2009)
- Professor, School of Engineering, Zhejiang Normal University (Since 2013)
🏆 Achievements & Contributions:
- 📚 Published over 100 research articles
- 🔬 Invented approximately 20 innovations
- 🛠️ Expertise in smart testing, signal processing, mechanical dynamics, condition monitoring, and fault diagnosis
- 📝 Editor of Journal of Vibration, Measurement & Diagnosis
🏅 Awards & Honors:
- 🎖️ Recognized for contributions in mechanical engineering and diagnostics
- 🏅 Honored for advancements in fault diagnosis and condition monitoring
- 🔍 Acknowledged for outstanding research and academic contributions in mechanical dynamics
Publication Top Notes:
Compact multiphysics coupling modeling and analysis of self-excited vibration in maglev trains
Deep learning in industrial machinery: A critical review of bearing fault classification methods
Recursive prototypical network with coordinate attention: A model for few-shot cross-condition bearing fault diagnosis
Double attention-guided tree-inspired grade decision network: A method for bearing fault diagnosis of unbalanced samples under strong noise conditions
Cross-Conditions Fault Diagnosis of Rolling Bearing Based on Transitional Domain Adversarial Network