Dr Dongfang Zhao | Electrical Properties of Materials | Best Innovator Award
Dr. Dongfang Zhao is a dedicated researcher and lecturer at Shanghai Polytechnic University, specializing in artificial intelligence and mechanical fault diagnosis. Since 2014, he has advanced the frontiers of intelligent systems in industrial diagnostics. After completing his Ph.D. at Shanghai University in 2021, he pursued postdoctoral research at the Control Science and Engineering Research Station. Recognized nationally for his outstanding doctoral dissertation, Dr. Zhao continues to drive innovation in AI-enhanced fault detection. He has published 20+ SCI/EI-indexed papers, earning ~500 citations, and contributes significantly to academic and practical advancements.
Dr Dongfang Zhao, Shanghai Polytechnic University, China
Profile
Education
Dr. Zhao earned his Ph.D. in Measurement, Control, and Instrumentation from Shanghai University in 2021, where his dissertation was nominated for a national excellence award. He built his foundation in engineering and research through a series of rigorous academic training steps. Following graduation, he immediately joined a prestigious postdoctoral program in Control Science and Engineering. His academic path reflects a commitment to merging theoretical depth with applied innovation in intelligent diagnostics and control systems.
Experience
Currently serving as a lecturer and master’s supervisor at Shanghai Polytechnic University, Dr. Zhao is actively engaged in research and teaching. He completed a postdoctoral fellowship at the Control Science and Engineering Research Station of Shanghai University. Professionally, he has collaborated on several government and industrial research projects, including two National Natural Science Foundation of China initiatives and AI-enabled fault analysis programs. His diverse academic and industry-facing experiences reflect a holistic career in intelligent systems and real-world fault mitigation.
Contributions
Dr. Dongfang Zhao has developed three advanced AI models tailored to tackle diverse industrial diagnostic challenges. DRANet enhances fault detection accuracy under strong noise , while DBANet ensures precise diagnosis under variable rotational speeds
using extreme multi-scale entropy. Furthermore, SCANet excels in unlabeled diagnostic scenarios
, reducing domain confusion and boosting generalization
. These innovations significantly advance smart maintenance, ensuring reliability, adaptability, and intelligence in complex mechanical systems—paving the way for next-generation predictive diagnostics in Industry 4.0
.