Prof. Dr. Weidong Jiao | Intelligent Fault Diagnosis | Best Researcher Award
Prof. Dr. Weidong Jiao, Zhejiang Normal University, China
Prof. Dr. Weidong Jiao (b. 1970, Wafangdian, China) is a distinguished mechanical engineer specializing in smart testing, signal processing, mechanical dynamics, and fault diagnosis. He earned his B.E. and M.E. from Gansu University of Technology and a Ph.D. in Mechanical Engineering from Zhejiang University (2004). He served at Jiaxing University (2004-2009) and has been a Professor at Zhejiang Normal University since 2013. With 100+ publications and 20 patents, he is an influential researcher and Editor of the Journal of Vibration, Measurement & Diagnosis. His work advances mechanical equipment reliability and diagnostics. ⚙️📡📖
Publication Profile
Academic Background 🎓
Prof. Dr. Weidong Jiao holds a strong academic foundation in mechanical and safety engineering. He obtained his B.E. and M.E. degrees from Gansu University of Technology and later earned his Ph.D. in Mechanical Engineering from Zhejiang University in 2004. His educational background aligns well with high-impact research in engineering.
Professional Experience 👨🏫
Prof. Jiao has held faculty positions at Jiaxing University and Zhejiang Normal University. Since 2013, he has served as a Professor at Zhejiang Normal University’s School of Engineering, showcasing his long-term commitment to research and academia.
Research Contributions 📚
Prof. Jiao has made significant contributions to mechanical engineering, particularly in smart testing, signal processing, mechanical dynamics, condition monitoring, and fault diagnosis of mechanical equipment. He has authored over 100 research articles and holds approximately 20 patents, demonstrating innovation and scholarly impact.
Research Focus 🔬⚙️
Prof. Dr. Weidong Jiao specializes in mechanical fault diagnosis, signal processing, and intelligent condition monitoring. His work includes bearing fault detection 🛠️, rotor fault diagnosis 🚀, and high-speed train instability analysis 🚆 using deep learning 🤖, wavelet transforms 📊, and graph neural networks. Additionally, he explores brain functional networks 🧠, mental fatigue detection 💤, and EEG-based fatigue analysis for human performance monitoring. His research spans mechanical engineering, artificial intelligence, and neuroscience, contributing to smart diagnostics and industrial safety. As an
Publication Top Notes
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“Novel Imbalanced Multi-Class Fault Diagnosis Method Using Transfer Learning and Oversampling Strategies-Based Multi-Layer Support Vector Machines (ML-SVMs)” (2024)
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“Advances and Challenges in the Hunting Instability Diagnosis of High-Speed Trains” (2024)
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“Efficient Fault Detection of Rotor Minor Inter-Turn Short Circuit in Induction Machines Using Wavelet Transform and Empirical Mode Decomposition” (2023)
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“Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model” (2022)
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“Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue” (2022)
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“Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification” (2021)
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“A Novel Rolling Bearing Defect Detection Method Based on Bispectrum Analysis and Cloud Model-Improved EEMD” (2020)
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“The Maximum Eigenvalue of the Brain Functional Network Adjacency Matrix: Meaning and Application in Mental Fatigue Evaluation” (2020)
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“Effects of Mental Fatigue on Small-World Brain Functional Network Organization” (2019)
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“A New Method for Automatically Modelling Brain Functional Networks” (2018)
