Abstract
Predictive maintenance of electric motors is a hot topic nowadays. Due to the extensive participation of these machines in industry, there is an increasing demand of graduate engineering students with skills in this area. In spite of this fact, in many Universities, engineering programs did not include specific courses in this specific area. In recent years, there has been a certain increase in the offer of courses devoted to electric motors condition monitoring. Due to the constant research dynamism of this area, these courses should be prepared to incorporate the latest technological advances in order to instruct the students on the use of the most recent technologies. This paper presents several laboratory sessions that have been built in the context of a course dealing with the maintenance of electric motors. The sessions are designed to facilitate the student participation, promoting the collaborative work and facilitating the use of information and communication technologies (ICTs). At the same time, they are conceived to instruct the students with the most recent techniques for electric motors condition monitoring.
Original language | English |
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Title of host publication | Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society |
Publisher | IEEE |
Pages | 3965-3970 |
Number of pages | 6 |
Volume | 2017-January |
ISBN (Electronic) | 9781538611272 |
DOIs | |
Publication status | Published - 15 Dec 2017 |
MoE publication type | A4 Conference publication |
Event | Annual Conference of the IEEE Industrial Electronics Society - Beijing, China Duration: 29 Oct 2017 → 1 Nov 2017 Conference number: 43 http://iecon2017.csp.escience.cn/ |
Publication series
Name | Proceedings of the Annual Conference of the IEEE Industrial Electronics Society |
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Publisher | IEEE |
ISSN (Print) | 1553-572X |
Conference
Conference | Annual Conference of the IEEE Industrial Electronics Society |
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Abbreviated title | IECON |
Country/Territory | China |
City | Beijing |
Period | 29/10/2017 → 01/11/2017 |
Internet address |
Keywords
- collaborative work
- education
- induction motors
- laboratory sessions
- predictive maintenance