Journal of AI, Data Science and Cyber Systems

Dr. Yingjun Zhao has long been engaged in research in the field of intelligent condition monitoring and fault diagnosis. His primary research interests include the theoretical foundations and methodologies of intelligent diagnosis for rotating machinery and industrial equipment, as well as key technologies for the engineering applications of artificial intelligence and deep learning. His research spans signal processing, feature extraction, intelligent classification algorithms, and industrial big data analysis, and is dedicated to addressing the challenges of equipment health management under complex operating conditions. This research provides support for the development strategies of high-end equipment manufacturing and smart manufacturing. He currently serves as a guest editor for the JBRES journal and has published numerous papers in international journals such as Measurement. He also serves as a invited reviewer for several international journals, including Mechanical Systems and Signal Processing, Measurement, Engineering Applications of Artificial Intelligence, Expert Systems with Applications, and Neurocomputing.

• Equipment Fault Diagnosis and Condition Monitoring, Digital Signal Processing, Artificial Intelligence and Network Research