Use of Smart Diagnosis Systems for Education on Electric Motors Predictive Maintenance

Predictive maintenance of electric motors is a field of increasing importance due to the vast participation of these machines in many industrial processes, where they drive a diversity of loads. Students of Electrical Engineering must be familiarized with the available techniques for the maintenance of electric motors since most of them will be surely dealing with these machines during their professional life. In this regard, it is very important to learn how different widely used techniques such as current monitoring or infrared data analysis, or other recent techniques such as stray flux monitoring, operate. Due to this, it is important to include contents related to predictive maintenance techniques in course programs addressed to Electrical Engineering students. In this context, this paper proposes an innovative idea that can be helpful to teach the students on the operation of these techniques. The idea relies on using smart diagnosis systems that combine the application of different predictive maintenance techniques in a single device. In the paper, the application of one of these systems, that was developed by the authors in the content of recent research projects is presented. The use of such system is proposed within the context of two courses taught by the authors, in which these types of contents are taught. The utilization of the system shows a high potential for enabling a better assimilation of the taught concepts by the students.