1. Proceedings
  2. I3M
  3. 2021
  4. EMSS
  5. 10.46354/i3m.2021.emss.027

Virtual Reality System for training in the detection
and solution of failures in induction motors

  • a Gustavo Caiza,
  • b Marco Riofrio-Morales,
  • c Veronica Gallo C.,
  • d Santiago Alvarez-T.,
  • e Wilson O. Lopez,
  • f Marcelo V. Garcia 
  • a Universidad Politecnica Salesiana, UPS, Rumichaca y Moran Valverde av., Quito, 170146, Ecuador
  • b  Universidad Tecnica de Ambato, UTA, Colombia Av. and Chile st., Ambato, 180103, Ecuador 
  • c,d  Instituto Superior Tecnológico María Natalia Vaca, ISTMNV, Bolivariana Av. and El Condor Av., Ambato, 180205, Ecuador 
  • e  Universidad Tecnologica Indoamerica, UTI, Antonio Clavijo st., Ambato, 180103, Ecuador (f)Universidad Tecnica de Ambato, UTA, Colombia Av. and Chile st., Ambato, 180103, Ecuador
  • f  University of Basque Country, UPV/EHU, Alameda Urquijo, Bilbao, 48013, Spain
Cite as
Caiza G., Riofrio-Morales M., Gallo V.C., Alvarez-T. S, Lopez W.O., Garcia M.V. (2021). Virtual Reality System for training in the detection and solution of failures in induction motors. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 199-207. DOI: https://doi.org/10.46354/i3m.2021.emss.027

Abstract

The changing industrial world in which we find ourselves has forced companies to evolve technologically, restructuring their processes and improving their human resources skills. The acceptance by management of a fourth industrial revolution in transition to a fifth has led them to look for an economical way to stay updated and with the necessary skills to optimize their production chain. This work presents the development of virtual reality (VR) system for training in detecting faults in three-phase electric motors. A sample of 30 people was used, homogeneously divided into a control group and an experimental group. To evaluate the VR systems usability, the System Usability Scale (SUS) was used, obtaining an average value of 73.33, classifying the system as efficient for the proposed task. On the other hand, in terms of time and knowledge retention, the performance of this system was compared with the execution of a conventional one. For the training time, an optimization of 57.73% was obtained, while through a p-value of 0.000003, it was confirmed that this VR system provides a novel teaching methodology for the instruction and retention of technical knowledge.

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