The need to have human resources trained efficiently is imperative. However, most industries do not give it the necessary importance. Similarly, staying technologically up-to-date allows the industrial sector to optimize its resources and satisfy the needs of its customers. This research presents the development of a virtual reality (VR) system for learning fault detection and correction in induction motors. It has been compared to a conventional methodology in terms of time and acquired knowledge. A 58% reduction was observed in the first parameter, while for the second, a p-value of 0.0000005 was obtained, thus demonstrating that this system is more efficient than conventional training in terms of learning. Finally, its usability was evaluated with the VRUSE method, with averages above 3.8 and standard deviations below 0.70.

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