Lecturer at Shanghai Polytechnic University, China
Dr. Dongfang Zhao is currently serving as a lecturer and master’s supervisor at Shanghai Polytechnic University. He embarked on his research journey in artificial intelligence and mechanical fault diagnosis in 2014. Earning his Ph.D. from Shanghai University in 2021, he immediately pursued postdoctoral research at the Control Science and Engineering Research Station. His doctoral dissertation was distinguished with a nomination for the National Outstanding Doctoral Dissertation Award in the field of measurement control and instrumentation.
👨🔬 Profile
ORCID
🏆 Suitable for “Best Researcher Award”
Dr. Dongfang Zhao demonstrates exceptional merit for the Best Researcher Award through his impactful contributions in artificial intelligence and mechanical fault diagnosis. With a rapidly evolving research profile, he has delivered high-quality publications, led prestigious projects, and developed novel AI diagnostic frameworks. His work significantly advances diagnostic accuracy and robustness in complex environments, positioning him as a leading voice in his field.
🎓 Education
Dr. Zhao obtained his Ph.D. from Shanghai University in 2021. His academic journey reflects a deep and continuous commitment to advanced research in AI applications for engineering diagnostics. His dissertation stood out nationally in the field, affirming both the quality and relevance of his academic training.
💼 Experience
Dr. Zhao has built a strong professional trajectory by joining leading research stations and universities in China. Since October 2024, he has held the position of lecturer at Shanghai Polytechnic University. His prior engagement as a postdoctoral researcher at Shanghai University significantly contributed to national-level research projects, and he continues to mentor students and lead research as a master’s supervisor.
🔬 Research Interest
Dr. Zhao’s research spans Artificial Intelligence, Mechanical Fault Diagnosis, and Control Engineering. His work targets real-world engineering problems, especially fault detection under variable operational conditions. He introduced networks like DRANet, DBANet, and SCANet, each addressing specific challenges such as strong noise environments, variable speeds, and unlabeled data — critical innovations in industrial AI.
📚 Publications
Dr. Zhao has published more than 20 high-quality SCI/EI papers, with over 10 as first or corresponding author. His research appears in reputed journals like Advanced Engineering Informatics, Computers and Electronics in Agriculture, and IEEE Journals, and his work has received nearly 500 citations globally, reflecting its influence.
Notable Published Journals (with Year):
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Zhao, D. (2021). “AI-based DRANet for Noisy Fault Detection.” Advanced Engineering Informatics
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Zhao, D. (2022). “Extreme Multi-Scale Entropy-Based Fault Diagnosis.” IEEE Transactions on Instrumentation and Measurement
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Zhao, D. (2023). “SCANet: Unlabeled Fault Recognition Network.” Computers and Electronics in Agriculture