Virtual Reality System for Training in the Detection and Solution of Failures in Induction Motors

Gustavo Javier Caiza Guanochanga, Marco Riofrio Morales, Veronica Gallo, Santiago Alvarez, Marcelo Vladimir García Sánchez

Research output: Contribution to conferencePaper

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.
Translated title of the contributionSistema de Realidad Virtual para Entrenamiento en Detección y Solución de Fallas en Motores de Inducción
Original languageEnglish (US)
StatePublished - 17 Sep 2021
EventInternational Multidisciplinary Modeling & Simulation Multiconference (I3M 2021) - PL
Duration: 15 Sep 202117 Sep 2021
https://www.msc-les.org/i3m2021/

Conference

ConferenceInternational Multidisciplinary Modeling & Simulation Multiconference (I3M 2021)
Period15/09/2117/09/21
Internet address

Keywords

  • Virtual reality
  • Industrial training
  • Optimization
  • System usability scale (sus)
  • Induction motors

CACES Knowledge Areas

  • 417A Electronics, Automation and Sound

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