Artificial intelligence in monitoring and diagnostics of electrical energy conversion systems

Abstract

Diagnostics and prognostics of electrical energy conversion systems are moving forward with the rapid development of IT and artificial intelligence possibilities. This also broadens the horizons for classical and advanced condition and operation monitoring techniques, resulting in more accurate fault detection, degradation prognosis and calculation of remaining life of energy conversion systems, utilized in every aspect and field of industry today. This paper gives an overview of the necessity for condition monitoring and diagnostics of the mentioned systems, explaining the classical and advanced techniques for diagnostics. Methodology to diagnose and prognose the energy conversion units, where classical maintenance techniques are not sufficient in the economic, environmental and safety reasons is proposed. An extensive state of art in the field of diagnostics, regarding the aforementioned problems and techniques is provided.