Assoc. Prof. Dr. Xiaoli Zhao | Diagnostics | Best Researcher Award
Associate Professor | Nanjing University of Science and Technology | China
Xiaoli Zhao is an Associate Professor at the School of Mechanical Engineering, Nanjing University of Science and Technology, with research spanning intelligent diagnostics, prognostics and health management for electromechanical and hydraulic systems, artificial intelligence, signal processing, digital twins, and intelligent robotics. He earned his PhD in mechanical engineering from Southeast University and carried out part of his doctoral research as a visiting scholar at the University of British Columbia in Canada. He later completed postdoctoral training at Nanjing University of Science and Technology under the supervision of Professor Yao Jianyong and at Nanjing University of Aeronautics and Astronautics under Academician Chunsheng Zhao. His academic background also includes degrees from Lanzhou University of Technology and Chizhou University. He has published more than 100 papers in high-impact journals and conferences such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, IEEE/ASME Transactions on Mechatronics, and IEEE Transactions on Instrumentation and Measurement, which have collectively earned 2,469 citations by 1,897 documents across 84 publications. His work focuses on predictive maintenance, intelligent operation and health management, machine learning, big data, computer vision, intelligent sensing, instrumentation, robotics, and cyber-physical systems. He has been recognized among the World’s Top 2 Percent Most Cited Scientists by Stanford University. His editorial service includes roles as associate editor or editorial board member for journals such as International Journal of Acoustics and Vibration, Proceedings of the IMechE Part C, International Journal of Hydromechatronics, and Scientific Reports. Through his interdisciplinary contributions combining mechanical engineering with artificial intelligence and digital technologies, he advances the development of intelligent systems for reliability, efficiency, and innovation in modern industry.
Profile: Scopus | Orcid
Featured Publications
Zhao, X., Zhu, X., Liu, J., Hu, Y., Gao, T., Zhao, L., Yao, J., & Liu, Z. (2024). Model-assisted multi-source fusion hypergraph convolutional neural networks for intelligent few-shot fault diagnosis to electro-hydrostatic actuator. Information Fusion, 104, 102186.
Zhao, X., Hu, Y., Liu, J., Yao, J., Deng, W., Hu, J., Zhao, Z., & Yan, X. (2024). A novel intelligent multicross domain fault diagnosis of servo motor-bearing system based on domain generalized graph convolution autoencoder. Structural Health Monitoring.
Zhao, X., Song, Y., Hu, Y., He, X., Zhang, Z., Hu, J., Yao, J., Ding, P., & Feng, K. (2025). A new intelligent recognition method for surface electromyography in IoT systems using OmniXceptionDBN. IEEE Internet of Things Journal, 12(14), 28445–28453.
Hu, Y., Song, Y., He, X., Zhao, X., Yang, X., Yao, J., Wang, Z., Pei, H., & Hu, C. (2025). MAACCN: An intelligent decoupling diagnosis method for compound faults in electrohydrostatic actuators. IEEE Transactions on Instrumentation and Measurement, 74, Article 3532611.
He, X., Zhao, C., Li, S., Zhao, X., Yang, X., Song, Y., & Yao, J. (2025). Diffusion-enhanced dual-domain adversarial network: A zero-shot fault diagnosis method for electrohydrostatic actuators. IEEE Transactions on Instrumentation and Measurement, 74, 1–9.