Application of Augmented Reality for Aviation Equipment Inspection and Maintenance Training
Semantic Scholar · Article (International Conference on Applied System Innovation) · 24c7965e516dc81b09daf812a7d30b8da10ed568 · Published 2022-04-22 · International Conference on Applied System Innovation · 3 authors
Abstract and citation only, verbatim from Semantic Scholar; full text lives there. All credit to the authors and International Conference on Applied System Innovation.
Abstract
In the procedure of aviation equipment maintenance, a healthy status and prior knowledge are essential to every person. Making the newcomers get familiar with their tasks rapidly and reducing human error are important issues. Augmented reality (AR) integrated with the popular artificial intelligence (AI) technology can provide smart system inspections and train maintenance professionals on how to perform important maintenance procedures effectively and accurately. Utilization of the AR not only replaces classic manual training but also provides high mobility pre-training opportunities. In other words, AR is capable of taking equipment maintenance to the next level. In this paper, an application for aviation equipment inspection is developed. By using AR and AI, traditional standard operation procedures (SOP) can be visualized and standardized. Therefore, people who are not familiar with the maintenance procedure can still finish the standard inspections. The developed software gives a great contribution to human resources for maintenance training. Demonstrations including turbofan and landing gear are provided by using HoloLens 2 to illustrate the application novelty. Finally, pros and cons regarding maintenance using AR are also discussed.
Authors
- Chao-Chung Peng
- Ai-Chi Chang
- Yuwen Chu
Keywords
- Engineering
- Computer Science
Citation
Chao-Chung Peng, Ai-Chi Chang, Yuwen Chu (2022). Application of Augmented Reality for Aviation Equipment Inspection and Maintenance Training. International Conference on Applied System Innovation. Semantic Scholar ID 24c7965e516dc81b09daf812a7d30b8da10ed568. https://doi.org/10.1109/ICASI55125.2022.9774490 ↗