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Design and Implementation of an Augmented Reality-based Interactive and Real-time Wind Turbine Maintenance Auxiliary Platform System.
- Source :
- Sensors & Materials; 2023, Vol. 35 Issue 11, Part 4, p3969-3984, 16p
- Publication Year :
- 2023
-
Abstract
- To achieve the goal of net zero emissions by 2050, Taiwan's government is committed to aggressively developing renewable energy sources, with wind power being among the top three. However, when wind turbines experience failures, it is common to shut them down and call for professional help to troubleshoot, leading to significant losses due to transportation costs and downtime. Traditional maintenance methods rely on expertise and are limited by paper manuals and screen-based tutorials. As Industry 4.0 progresses, IoT technology is being increasingly adopted across industries, leading to greater automation. Augmented reality (AR) technology is also gaining recognition as science and technology rapidly advance. AR technology adds virtual objects to the physical world and presents the potential to simplify tasks that once required significant time and effort. Additionally, it provides real-time information to technicians and has become an indispensable auxiliary system in various sectors. In this study, we aim to develop an AR-based, interactive, and real-time wind turbine maintenance auxiliary platform system using Unity3D and Vuforia. The system can control and repair wind turbines, monitor operational data and conditions in real time, and alert users to maintenance and repair tasks in the event of failures. This AR maintenance assistance platform also includes a remote expert system. When users are unable to troubleshoot issues themselves, they can connect with remote experienced experts for support, increasing wind farm efficiency and enabling real-time monitoring. The presented AR system can assist operations and maintenance (O&M) of a wind farm in reducing the maintenance cost and time, and thus increase the efficiency and output yield of the wind farm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09144935
- Volume :
- 35
- Issue :
- 11, Part 4
- Database :
- Complementary Index
- Journal :
- Sensors & Materials
- Publication Type :
- Academic Journal
- Accession number :
- 174150065
- Full Text :
- https://doi.org/10.18494/SAM4668