Dr. Yingjun Zhao has long been engaged in research within the field of intelligent condition monitoring and fault diagnosis. His primary focus lies in the theoretical foundations and methodologies for intelligent diagnostics of rotating machinery and industrial equipment, as well as key technologies involving artificial intelligence and deep learning in engineering applications. His research encompasses signal processing, feature extraction, intelligent classification algorithms, and industrial big data analysis, dedicated to addressing challenges in equipment health management under complex operating conditions. This work supports the development strategies for high-end equipment manufacturing and intelligent manufacturing. He has published multiple papers in international journals such as Measurement and serves as an invited reviewer for several international journals, including Mechanical Systems and Signal Processing, Measurement, Engineering Applications of Artificial Intelligence, Expert Systems with Applications, and Neurocomputing.