KEYNOTES > Prof. Yassine Amirat

Failure Detection in Electromechanical Systems: Unveiling Challenges, Trends, and Advanced Signal Processing Solutions

Yassine Amirat, IEEE Senior Member

ISEN Ouest, Brest Campus, France

Abstract

Most of the techniques used for failure detection and diagnosis rely on spectral analysis, such as Fourier analysis, or methods derived from it. Although these techniques exhibit good results in stationary conditions, they are not well-suited for most electromechanical systems (i.e., electric machines and drives applications). Indeed, these applications are predominantly nonstationary due to transients or variable-speed operations. In this context, the involved signals are usually nonstationary, embedded in noise, and can contain closely spaced frequencies. It is then obvious that failure detection and diagnosis in such applications are challenging tasks that need to use advanced signal processing tools.

This plenary intends, therefore, to focus on state-of-the-art research and development as well as future trends in electromechanical systems condition monitoring, and attempts to shed light on prospective challenges, providing insight into emerging techniques that may address these issues.

Biography:

Yassine Amirat received the B.Sc. and M.Sc. degrees in electrical engineering from the University of Annaba, Annaba, in 1994 and 1997, respectively. He was a lecturer at Annaba University from 2000 to 2010, and a senior consultant at the Algerian Institute of Petroleum (IAP Hassi Messaoud) from 2001 to 2010. He obtained the Ph.D. and the HDR (Habilitation à diriger des recherches) degrees at the University of Brest, Brest, France in 2011 and 2019 respectively.

He is currently a Professor of Electrical Engineering at ISEN Yncréa Ouest, Brest (France). He is an IEEE Senior Member, an associate Editor for Springer-Nature Electrical Engineering Journal, and an Editor of the MDPI Journal of Marine Science and Engineering.  His main research interests include electrical machines faults detection and diagnosis, and advanced signal processing and statistics for power systems monitoring. He is also interested in renewable energy applications: wind turbines, marine current turbines, and energy management systems in hybrid generation systems.