An Immersive Training Approach for Induction Motor Fault Detection and Troubleshooting

Gustavo Caiza, Marco Riofrio-Morales, Angel Robalino-Lopez, Orlando R. Toscano, Marcelo V. Garcia, Jose E. Naranjo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Industry 4.0 has gained drive in the last few years since most companies at the industrial level need to update and optimize their production chain. Restructuring their processes and improving their human resources skills is imperative if they want to remain competitive. This research presents the development of a virtual reality system for induction motor troubleshooting training. Two sample groups were taken as references. The first one was trained with the VR system, while the second group worked with a conventional methodology. With the use of the proposed system, there was a time reduction of 57.73%. In terms of knowledge acquisition, it was possible to confirm, with a p-value lower than 0.05, that the VR system is more efficient than the conventional methodology. Finally, the system’s usability was evaluated utilizing the System Usability Scale (SUS), obtaining an average value of 73.33.

Original languageEnglish
Title of host publicationAugmented Reality, Virtual Reality, and Computer Graphics - 8th International Conference, AVR 2021, Proceedings
EditorsLucio Tommaso De Paolis, Pasquale Arpaia, Patrick Bourdot
PublisherSpringer Science and Business Media Deutschland GmbH
Pages499-510
Number of pages12
ISBN (Print)9783030875947
DOIs
StatePublished - 2021
Event8th International Conference on Augmented Reality, Virtual Reality and Computer Graphics, AVR 2021 - Virtual, Online
Duration: 7 Sep 202110 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12980 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Augmented Reality, Virtual Reality and Computer Graphics, AVR 2021
CityVirtual, Online
Period7/09/2110/09/21

Bibliographical note

Funding Information:
The authors recognize the supported bringing by Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project CONIN-P-256-2019.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Induction motors
  • Industrial training
  • Optimization
  • Virtual reality

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