Induction motors have applications in many engineering areas including high speed train, aerospace, electric vehicles, robotics, machine tool, etc. Nowadays, there is an increasing need for condition monitoring and prognostics of the induction motors due to their increasing range of applications and the high impact of their failure. Statistically, rolling-element bearing and stator winding failures are the primary causes of unexpected breakdown in induction motors. Due to the application of rolling-element bearings in almost all of the rotating machines, many condition monitoring and prognostic methods for bearings have been developed in the last four decades and published in literature. However, despite the high impact of winding failures in induction motors and their costly replacement, the amount of research in diagnosis and prognosis of winding faults remains limited. This presentation focuses on exploring a current and voltage-based fault diagnosis solution for early diagnosis of winding faults in induction motors. Three common insulation faults, namely (1) turn-to-turn, (2) inter-turn and (3) turn-to-earth were induced in an induction motor with three different levels of severity. Number of experiments have been carried out with different working regimes, comprising healthy and the aforementioned winding faults.  Both time-domain and frequency-domain features have been extracted from the current and voltage signals and their symmetrical components. Afterwards, the effectiveness of these features for distinguishing both healthy and faulty states is evaluated. The experimental results show that the combination of the extracted features can provide a comprehensive fault diagnosis scheme for winding insulation of induction motors.

Agusmian Partogi Ompusunggu is a senior research engineer at Flanders Make vzw, the strategic research centre for the manufacturing industry in Flanders, Belgium. He has diverse research experiences including (i) Condition monitoring, prognostics and health management, (ii) Dynamics & Vibration analysis, (iii) Industrial big data analytics and advanced signal processing, (iv) Tribology and (v) Advanced manufacturing technologies. He holds a Ph.D. degree in mechanical engineering from the University of Leuven (KU Leuven), Belgium. He earned his B.Eng. degree in 2004 and his M.Eng. degree in 2006 both from Institut Teknologi Bandung (ITB), Bandung, Indonesia. Dr. Ompusunggu has been working in different R&D projects together with high-tech companies in Flanders. He has been invited as speaker in national and international events. He has published more than 30 technical papers in scientific journals and conference proceedings.