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Phong B. Dao

Prof. Dr. Phong B. Dao

Department of Robotics and Mechatronics,  AGH University of Science and Technology
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Phong B. Dao received the Engineer degree in Cybernetics in 2001, the M.Sc. degree in Instrumentation and Control in 2004, both from Hanoi University of Science and Technology in Vietnam, and the Ph.D. degree in Control Engineering in 2011 from the University of Twente, the Netherlands. In May 2020, Dr. Dao received the degree of D.Sc. (Habilitation) in Mechanical Engineering from the AGH University of Science and Technology, Poland. He is currently an Associate Professor at the Department of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics of the AGH University of Science and Technology. His research interests include structural health monitoring (SHM), non-destructive testing (NDT), wind turbine condition monitoring and fault diagnosis, statistical time series methods for SHM & NDT, machine learning methods for SHM & NDT, advanced signal/data processing, intelligent control, agent-based control, and mechatronics.

Research Keywords & Expertise

Mechatronics
intelligent control
condition monitoring a...
Structural Health Moni...
advanced signal/data p...

Fingerprints

33%
condition monitoring and fault diagnosis
28%
Structural Health Monitoring (SHM)
6%
Mechatronics

Short Biography

Phong B. Dao received the Engineer degree in Cybernetics in 2001, the M.Sc. degree in Instrumentation and Control in 2004, both from Hanoi University of Science and Technology in Vietnam, and the Ph.D. degree in Control Engineering in 2011 from the University of Twente, the Netherlands. In May 2020, Dr. Dao received the degree of D.Sc. (Habilitation) in Mechanical Engineering from the AGH University of Science and Technology, Poland. He is currently an Associate Professor at the Department of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics of the AGH University of Science and Technology. His research interests include structural health monitoring (SHM), non-destructive testing (NDT), wind turbine condition monitoring and fault diagnosis, statistical time series methods for SHM & NDT, machine learning methods for SHM & NDT, advanced signal/data processing, intelligent control, agent-based control, and mechatronics.