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Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles

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  • Krzysztof Lalik

    (Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

  • Filip Wątorek

    (Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

Abstract

The concept of predictive and preventive maintenance and constant monitoring of the technical condition of industrial machinery is currently being greatly improved by the development of artificial intelligence and deep learning algorithms in particular. The advancement of such methods can vastly improve the overall effectiveness and efficiency of systems designed for wear analysis and detection of vibrations that can indicate changes in the physical structure of the industrial components such as bearings, motor shafts, and housing, as well as other parts involved in rotary movement. Recently this concept was also adapted to the field of renewable energy and the automotive industry. The core of the presented prototype is an innovative interface interconnected with augmented reality (AR). The proposed integration of AR goggles allowed for constructing a platform that could acquire data used in rotary components technical evaluation and that could enable direct interaction with the user. The presented platform allows for the utilization of artificial intelligence to analyze vibrations generated by the rotary drive system to determine the technical condition of a wind turbine model monitored by an image processing system that measures frequencies generated by the machine.

Suggested Citation

  • Krzysztof Lalik & Filip Wątorek, 2021. "Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles," Energies, MDPI, vol. 14(22), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7632-:d:679634
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    References listed on IDEAS

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    7. Ilona Kieliba & Ireneusz Dominik & Krzysztof Lalik & Thorsten Tonnesen & Jacek Szczerba & Reiner Telle, 2021. "Self-Excited Acoustical System Frequency Monitoring for Refractory Concrete under Uniaxial Compression," Energies, MDPI, vol. 14(8), pages 1-17, April.
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    1. David Pérez Granados & Mauricio Alberto Ortega Ruiz & Joel Moreira Acosta & Sergio Arturo Gama Lara & Roberto Adrián González Domínguez & Pedro Jacinto Páramo Kañetas, 2023. "A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions," Energies, MDPI, vol. 16(5), pages 1-17, February.

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