2022
Cruz, David Checa; Saucedo-Dorantes, Juan José; Ríos, Roque Alfredo Osorno; Antonio-Daviu, José Alfonso; Bustillo, Andrés
Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors Journal Article
In: Applied Sciences, vol. 12, no. 1, pp. 414, 2022, ISSN: 2076-3417.
Abstract | Links | BibTeX | Tags: eye tracking, fault detection, FFT, induction motors, SELECTED, Virtual Reality
@article{Cruz2022,
title = {Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors},
author = {David Checa Cruz and Juan José Saucedo-Dorantes and Roque Alfredo Osorno Ríos and José Alfonso Antonio-Daviu and Andrés Bustillo},
url = {https://www.mdpi.com/2076-3417/12/1/414},
doi = {10.3390/app12010414},
issn = {2076-3417},
year = {2022},
date = {2022-01-01},
journal = {Applied Sciences},
volume = {12},
number = {1},
pages = {414},
abstract = {The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. Moreover, not all educational centers or organizations are able to incorporate specialized labs or equipment for training and instruction. Using VR applications, it is possible to reproduce training programs with a high rate of similarity to real programs, filling the gap in traditional training. In addition, it reduces unnecessary investment and prevents economic losses, avoiding unnecessary damage to laboratory equipment. The contribution of this work focuses on the development of a VR-based teaching and training application for the condition-based maintenance of induction motors. The novelty of this research relies mainly on the use of natural interactions with the VR environment and the design’s optimization of the VR application in terms of the proposed teaching topics. The application is comprised of two training modules. The first module is focused on the main components of induction motors, the assembly of workbenches and familiarization with induction motor components. The second module employs motor current signature analysis (MCSA) to detect induction motor failures, such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Finally, the usability of this VR tool has been validated with both graduate and undergraduate students, assuring the suitability of this tool for: (1) learning basic knowledge and (2) training in practical skills related to the condition-based maintenance of induction motors.},
keywords = {eye tracking, fault detection, FFT, induction motors, SELECTED, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. Moreover, not all educational centers or organizations are able to incorporate specialized labs or equipment for training and instruction. Using VR applications, it is possible to reproduce training programs with a high rate of similarity to real programs, filling the gap in traditional training. In addition, it reduces unnecessary investment and prevents economic losses, avoiding unnecessary damage to laboratory equipment. The contribution of this work focuses on the development of a VR-based teaching and training application for the condition-based maintenance of induction motors. The novelty of this research relies mainly on the use of natural interactions with the VR environment and the design’s optimization of the VR application in terms of the proposed teaching topics. The application is comprised of two training modules. The first module is focused on the main components of induction motors, the assembly of workbenches and familiarization with induction motor components. The second module employs motor current signature analysis (MCSA) to detect induction motor failures, such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Finally, the usability of this VR tool has been validated with both graduate and undergraduate students, assuring the suitability of this tool for: (1) learning basic knowledge and (2) training in practical skills related to the condition-based maintenance of induction motors.