A Unified Framework for Augmented Reality and Knowledge-Based Systems in Maintaining Aircra

Authors

  • Geun-Sik Jo INHA University
  • Kyeong-Jin Oh INHA University
  • Inay Ha INHA University
  • Kee-Sung Lee INHA University
  • Myung-Duk Hong INHA University
  • Ulrich Neumann University of Southern California
  • Suya You University of Southern California

DOI:

https://doi.org/10.1609/aaai.v28i2.19023

Abstract

Aircraft maintenance and training play one of the most important roles in ensuring flight safety. The maintenance process usually involves massive numbers of components and substantial procedural knowledge of maintenance procedures. Maintenance tasks require technicians to follow rigorous procedures to prevent operational errors in the maintenance process. In addition, the maintenance time is a cost-sensitive issue for airlines. This paper proposes intelligent augmented reality (IAR) system to minimize operation errors and time-related costs and help aircraft technicians cope with complex tasks by using an intuitive UI/UX interface for their maintenance tasks. The IAR system is composed mainly of three major modules: 1) the AR module 2) the knowledge-based system (KBS) module 3) a unified platform with an integrated UI/UX module between the AR and KBS modules. The AR module addresses vision-based tracking, annotation, and recognition. The KBS module deals with ontology-based resources and context management. Overall testing of the IAR system is conducted at Korea Air Lines (KAL) hangars. Tasks involving the removal and installation of pitch trimmers in landing gear are selected for benchmarking purposes, and according to the results, the proposed IAR system can help technicians to be more effective and accurate in performing their maintenance tasks.

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Published

2014-07-27

How to Cite

Jo, G.-S., Oh, K.-J., Ha, I., Lee, K.-S., Hong, M.-D., Neumann, U., & You, S. (2014). A Unified Framework for Augmented Reality and Knowledge-Based Systems in Maintaining Aircra. Proceedings of the AAAI Conference on Artificial Intelligence, 28(2), 2990-2997. https://doi.org/10.1609/aaai.v28i2.19023