2026-07-30T22:56:32Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73352023-03-21T09:05:07Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

00925njm 22002777a 4500 dc Checa Cruz, David author Saucedo Dorantes, Juan José author Osornio-Ríos, Roque Alfredo author Antonio-Daviu, José Alfonso author Bustillo Iglesias, Andrés author 2022-01 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. http://hdl.handle.net/10259/7335 10.3390/app12010414 2076-3417 Virtual reality Induction motors Fault detection FFT Eye tracking Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors