Towards Machine Learning Maintenance in Airport Infrastructure
Keywords:
Maintenance, Airport Infrastructure, Artificial Intelligence, Machine Learning, BIMSynopsis
This is a Chapter in:
Book:
Competitive Tools, Techniques, and Methods
Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9
Series:
Chronicle of Computing
Chapter Abstract:
The total operating costs of airport infrastructures include substantial upkeep expenses. Maintenance management methods are becoming increasingly important because any damage can lead to significant consequences at the airport. Many classic maintenance methods, like "run to failure" or preventive methods, are lacking in different fields because they're expensive and show a non-maintenance management approach. Many methods related to BIM will detect all possible failures and resolve them before they occur, a type of management known as "predictive maintenance." Predictive maintenance enables you to maximize the availability of engineering systems, prevent downtime, reduce downtime, and increase safety.
Cite this paper as:
Lahna T. (2024). Towards Machine Learning Maintenance in Airport Infrastructure. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIS24#21. https://doi.org/10.55432/978-1-6692-0007-9_17
Presented at:
The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online on October 2, 2024
Contact:
Tarik Lahna
lahnatk@ucla.edu
References
Kovacs, G (2019). Research methods in humanitarian logistics. Journal of humanitarian logistics and supply chain management, 9 (3), 325-331
BIFM, 2012. BIM and FM: Bridging the gap for success, s.l.: British Institute of Facilities Management.
Burbidge, R (2016). Adapting European Airports to a Changing Climate
Eastman, C. T. P. S. R. L. K., 2008. BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractrors. 2nd ed. s.l.:Wiley, Hoboken, New Jersey
Coibion, O., Gorodnichenko Y., & Weber, M.(2020). The cost of the covid-19 crisis: Lockdowns, Macroeconomic expectations and Consumer spending. National bureau of economic research.
Gorse, C. & Highfield, D., 2009. Refurbishment and Upgrading of Buildings, Abingdon, U.K.: Spon Press.
Chen, M-Y., Chiang, H-S., Lugofer, E., & Egrioglu, E (2020). Deep learning: emerging trends, applications and research challenges.
Shu, Z. et al., 2016. Unsupervised 3D shape segmentation and co-segmentation via deep learning. Computer Aided Geometric Design, Volume 43, pp. 39-52.
Son, H., Kim, C. & Kim, C., 2013. Knowledge-based approach for 3D reconstruction of as- built industrial plant models from laser-scan data. Montreal, s.n., pp. 885-893.
Petritoli, E., Leccese, F., & Ciani, L. (2018). Reliability and maintenance analysis of unmanned aerial vehicles.
Mobley, R. (2002). Introduction to predictive maintenance.
Lee, KHY., & Scott, D. (2009). Overview of maintenance strategy, acceptable maintenance from a building maintenance operation perspective. Journal of Building Appraisal.
Kawashima, K., Kanai, S. & Date, H., 2014. Automatic recognition of piping system from large-scale terrestrial laser scanned point cloud. Journal of Computational Design and Engineering, Volume 1 (1), pp. 13-26.
Pukite, I., & Geipele, I. (2017). Different approaches to building management and maintenance meaning explanation.
Koening, F., Found, PA., & Kumar, M. (2019). Innovative airport 4.0 condition-based maintenance system for baggage handling DCV systems
Veldhuis, H. & Vosselman, G., 1998. The 3D reconstruction of straight and curved pipes using digital line photogrammetry. ISPRS Journal of Photogrammetry and Remote Sensing, 53, pp. 6-16.
Luan, X., Wang, H., Ou, W., & Liu, L. (2017). Face liveness detection with recaptured feature extraction. International conference on Security, Pattern analysis and Cybernetics (SPAC)
Burnaev, EV. Algorithm foundations of predictive analytics in industrial engineering design. Journal of communications technology and electronics.
Trisco Systems. (2014). www.triscosystems.com
