Induction Motors VR

This simulator allows students Induction motor basics training, MCSA to detect faults in induction motors and Thermography for the detection of faults in kinematic chains


  • Induction motor basics training: This first module focuses on the main components of induction motors, the assembly of workbenches and familiarization with the elements of induction motors.
  • MCSA to detect faults in induction motors: The second module employs motor current signature analysis to detect induction motor faults such as broken rotor bars, unbalanced misalignments and gradual wear of gear teeth. The practical application of MCSA represents a challenging task and is crucial in the training of new maintenance technicians.
  • Thermography for the detection of faults in kinematic chains:This third module allows to learn how to detect seven electromechanical failures in a powertrain. The tool is based on real thermographic data from experiments on a real powertrain where thermal images were acquired with a low-cost infrared sensor.


Kinematic chains have a variety of applications, taking a fundamental role in many industrial processes. Given the importance of kinematic chains, different techniques for monitoring and maintaining these devices have been documented over the last years. Among those, the analysis of infrared thermal images has proven to be a non-invasive and efficient method for the detection of multiple electromechanical faults.

Virtual Reality (VR) is a recent technology that has not been used very often in industrial applications, nonetheless, VR can be an accessible and cheap way to assist in the training and capacitation process of industrial personnel on specific engineering topics. Nevertheless, most virtual environments are based on numerical simulations. This paper presents the design and development of a virtual environment for the detection of fourteen electromechanical fault cases in a kinematic chain.

The designed VR tool is based on real thermographic data from experiments performed on a real kinematic chain where thermal images were acquired with a 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.

Checa, D., Bustillo, A., Saucedo-Dorantes, J. J., Osornio-Rios, R. A., & Cruz-Albarran, I. A. (2021, October). Virtual reality-based tool applied in the teaching and training of condition-based maintenance in induction motors. In IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society (pp. 1-6). IEEE.

Checa, D., Saucedo-Dorantes, J. J., Osornio-Rios, R. A., Antonino-Daviu, J. A., & Bustillo, A. (2022). Virtual reality training application for the condition-based maintenance of induction motors. Applied Sciences, 12(1), 414.

Alvarado-Hernandez, A. I., Checa, D., Osornio-Rios, R. A., Bustillo, A., & Antonino-Daviu, J. A. (2022, September). Design and Development of Virtual Reality Application Based on Infrared Thermography for the Detection of Multiple Faults in Kinematic Chains. In 2022 International Conference on Electrical Machines (ICEM) (pp. 1569-1575). IEEE.

Project details

Introduction Motors VR







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