Biography

Widagdo assists industries in implementing electric motor fault diagnosis and predictive maintenance strategies through electrical signature analysis (ESA) and AI data-driven technologies. Previously, he did his doctoral research in the integration of model-driven and AI data-driven frameworks to build a robust induction motors fault diagnosis system. He believes that the predictive maintenance strategy keeps the electric motors running safely and efficiently, preventing unplanned downtimes.

Widagdo received a Ph.D.  in dynamic systems and control from the Department of Mechanical Engineering, National Taiwan University of Science and Technology. His domain of expertise is in the field of fault diagnosis, signal processing, and estimation theory. Currently, he mainly works with frequency and spectral analysis, electrical signature analysis, physical-based modeling, and system identification.

As a control engineer, he also loves archeology/history, Batman, comic books, action figures, LEGO, board games, and superhero graphic tees.