Maintenance 4.0: Automation of Aircraft Maintenance Operational Processes

Abstract

The advancement of technology is ramping up the pace of digitization and automation of aircraft maintenance activities, and with that, the stakeholders’ interest in high-level technology has also increased over the past few years. Thus, to stay relevant in the market and capable of competing, Aircraft Maintenance and Overhaul (MRO) companies must reshape and adapt to newer methodologies to enhance and enrich the quality of aviation and after-sales services. Operational processes are essential to any successful business because it plays a vital role in the efficient and effective functioning of the organization and structure of the enterprise. Hence this paper will focus on the possibilities of automation and data integration into the daily operational workflow, its contribution, and its influence on the industry. And since the initiation of industry 4.0, newer opportunities and possibilities have arisen to investigate decision-making algorithms, their influence on overall job quality and precision as well as the synergy between humans and machines and their cooperation in the operational areas of the maintenance process

Keywords

• Aircraft Maintenance
• Industry 4.0
• Automation
• Operational Process Development
• Artificial Intelligence

References

1. Acosta, G.G., Verucchi, C.J. and Gelso, E.R. (2006) ‘A current monitoring system for diagnosing electrical failures in induction motors’, Mechanical Systems and Signal Processing, 20(4), pp. 953–965.
2. Ahmed, U., Ali, F. and Jennions, I. (2021) ‘A review of aircraft auxiliary power unit faults, diagnostics and acoustic measurements’, Progress in Aerospace Sciences, 124, p. 100721.
3. Bello, R.-W. (2016) ‘Self Learning Computer Troubleshooting Expert System’.
4. Djenadic, S. et al. (2022) ‘Risk Evaluation: Brief Review and Innovation Model Based on Fuzzy Logic and MCDM’, Mathematics, 10(5), p. 811.
5. FAA (2011) ‘Destination2025’. Federal Aviation Administration. Available at: https://www.faa.gov/about/plans_reports/media/destination2025.pdf.
6. FAA (2016) ‘Air Carrier Maintenance Programs’. Federal Aviation Administration. Available at: https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_120-16G.pdf.
7. Huang, J., Chang, Q. and Arinez, J. (2020) ‘Product Completion Time Prediction Using A Hybrid Approach Combining Deep Learning and System Model’, Journal of Manufacturing Systems, 57, pp. 311–322. Available at: https://doi.org/10.1016/j.jmsy.2020.10.006.
8. Kolodner, J.L. (1992) ‘An introduction to case-based reasoning’, Artificial intelligence review, 6(1), pp. 3–34.

9. Li, J. and Lafond, D. (2023) ‘Hybrid Human-AI Forecasting for Task Duration Estimation in Ship Refit’, in G. Nicosia et al. (eds) Machine Learning, Optimization, and Data Science. Cham: Springer Nature Switzerland (Lecture Notes in Computer Science), pp. 558–572. Available at: https://doi.org/10.1007/978-3-031-25599-1_41.
10. Mate Labs (2017) Everything you need to know about Neural Networks | HackerNoon. Available at: https://hackernoon.com/everything-you-need-to-know-about-neural-networks-8988c3ee4491 (Accessed: 11 May 2022).
11. Moore, J. (2019) What is knowledge-based systems (KBS)? - Definition from WhatIs.com, SearchCIO. Available at: https://www.techtarget.com/searchcio/definition/knowledge-based-systems-KBS (Accessed: 9 May 2022).
12. Nandal, M., Mor, N. and Sood, H. (2021) ‘An overview of use of artificial neural network in sustainable transport system’, Computational methods and data engineering, pp. 83–91.
13. Pelt, M., Stamoulis, K. and Apostolidis, A. (2019) ‘Data analytics case studies in the maintenance, repair and overhaul (MRO) industry’, MATEC Web of Conferences, 304, p. 04005. Available at: https://doi.org/10.1051/matecconf/201930404005.
14. Pelt, M.M.J.M. and Stamoulis, K. (2018) ‘Data Mining for Aircraft Maintenance Repair and Overhaul (MRO) Process Optimization’, ISATECH 2018 [Preprint].
15. Pusztová, Ľ., Babič, F. and Paralič, J. (2021) ‘Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process’, Applied Sciences, 11(1), p. 292. Available at: https://doi.org/10.3390/app11010292.
16. Sahin, M., Kizilaslan, R. and Demirel, Ö.F. (2013) ‘Forecasting aviation spare parts demand using croston based methods and artificial neural networks’, Journal of Economic and Social Research, 15(2), p. 1.
17. Sasadmin (2014) Aircraft Maintenance Man Hour Planning Considerations • SASSofia. Available at: https://sassofia.com/blog/aircraft-maintenance-man-hour-planning-considerations/ (Accessed: 18 December 2022).
18. Scikit-learn (2019) Neural network models (supervised), scikit-learn. Available at: https://scikit-learn.org/0.21/_downloads/scikit-learn-docs.pdf (Accessed: 30 March 2023).
19. Sioson, D. (2020) ‘Introduction to Fuzzy Logic’, Medium, 12 August. Available at: https://medium.com/@siosond/introduction-to-fuzzy-logic-3664c610d98c (Accessed: 18 December 2022).
20. Xie, X. et al. (2020) ‘Research on fault diagnosis of aeroengine endoscopic detection based on CBR and RBR’, in Twelfth International Conference on Digital Image Processing (ICDIP 2020). International Society for Optics and Photonics, p. 115190X.
21. Zhou, R., Awasthi, A. and Stal-Le Cardinal, J. (2021) ‘The main trends for multi-tier supply chain in Industry 4.0 based on Natural Language Processing’, Computers in Industry, 125, p. 103369.