RT info:eu-repo/semantics/article
T1 A Virtual Reality Environment Based on Infrared Thermography for the Detection of Multiple Faults in Kinematic Chains
A1 Alvarado-Hernandez, Alvaro Ivan
A1 Checa Cruz, David
A1 Osornio-Ríos, Roque Alfredo
A1 Bustillo Iglesias, Andrés
A1 Antonino Daviu, Jose A.
K1 Digital image processing
K1 Fault detection
K1 Induction motors
K1 Infrared imaging
K1 Virtual reality
K1 Informática
K1 Computer science
AB Kinematic chains are crucial in numerous industrial settings, playing a key role in various processes. Over recent years, several methods have been developed to monitor and maintain these systems effectively. One notable method is the analysis of infrared thermal images, which serves as a non-invasive and effective approach for identifying various electromechanical issues. Additionally, Virtual Reality (VR) is a burgeoning technology that, despite its limited use in industrial contexts, offers a cost-effective and accessible solution for the training and education of industrial workers on specialized engineering subjects. Nevertheless, most virtual environments are based on numerical simulations. This paper presents the design and development of a Virtual Reality training module for the detection of fourteen electromechanical fault cases in a kinematic chain. The VR training tool developed is based on actual thermographic data derived from experiments conducted on an authentic kinematic chain. During these experiments, thermal images were captured using an low-cost infrared sensor. The thermographic images were processed by calculating the histogram and fifteen statistical indicators, which served to differentiate fault cases in the VR application. A comprehensive evaluation was carried out with a group of vocational students specialized in electrical and automation installations to determine the effectiveness and practicality of the VR training module.
PB MDPI
SN 2079-9292
YR 2024
FD 2024-06-22
LK http://hdl.handle.net/10259/9517
UL http://hdl.handle.net/10259/9517
LA eng
NO This research has been partially supported by Banco Santander under the scholarship program Santander Iberoamérica Research 2019/20. This investigation was partially supported by the REMAR Project (Ref. number CPP2022-009724) funded by the Ministry of Science and Innovation of Spain (MCIN/AEI/10.13039/501100011033) and by the European Union NextGenerationEU/PRTR.
DS Repositorio Institucional de la Universidad de Burgos
RD 24-nov-2024