Artificial Intelligence Supported Aircraft Maintenance Strategy Selection with q-Rung Orthopair Fuzzy TOPSIS Method

Abstract

In the aviation sector as unscheduled maintenance, repair and overhaul cost too much and these activities also negatively affect the prestige of the companies, deciding the most appropriate maintenance strategy is crucial. Today artificial intelligence methods, especially machine learning techniques facilitate failure detection and predict the wear and tear of the equipment before the occurrence of a serious failure. In this paper, artificial intelligence-supported corrective, predictive, and prescriptive maintenance methods are examined. Those most common aircraft maintenance approaches are compared regarding cost, reliability, failure detection, and downtime period using decision makers' subjective evaluations with the help of the q-rung orthopair fuzzy TOPSIS method which mitigates the drawbacks of uncertainty in human decision making. Stable and efficient results are obtained regarding the selection of an appropriate maintenance strategy. This article might be the first quantitative research that evaluates and compares AI-supported aircraft maintenance strategies.

Keywords

• Aircraft maintenance
• Decision making
• q-rung orthopair fuzzy TOPSIS
• Artificial intelligence

References

1. Adhikari, P. P., & Buderath, M. (2016). A framework for aircraft maintenance strategy including CBM. PHM Society European Conference,
2. Ahmadi, A., Söderholm, P., & Kumar, U. (2007). An overview of trends in aircraft maintenance program development: past, present, and future. European Safety and Reliability Conference: 25/06/2007-27/06/2007,
3. Amruthnath, N., & Gupta, T. (2018). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. 2018 5th international conference on industrial engineering and applications (ICIEA),
4. Andrade, P., Silva, C., Ribeiro, B., & Santos, B. F. (2021). Aircraft maintenance check scheduling using reinforcement learning. Aerospace, 8(4), 113.
5. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
6. Basora, L., Bry, P., Olive, X., & Freeman, F. (2021). Aircraft fleet health monitoring with anomaly detection techniques. Aerospace, 8(4), 103.
7. Basri, E. I., Razak, I. H. A., Ab-Samat, H., & Kamaruddin, S. (2017). Preventive maintenance (PM) planning: a review. Journal of quality in maintenance engineering.
8. Beliën, J., Cardoen, B., & Demeulemeester, E. (2012). Improving workforce scheduling of aircraft line maintenance at Sabena Technics. Interfaces, 42(4), 352-364.

9. Biswal, S., & Sabareesh, G. (2015). Design and development of a wind turbine test rig for condition monitoring studies. 2015 international conference on industrial instrumentation and control (icic),
10. Cawley, G. C., & Talbot, N. L. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. The Journal of Machine Learning Research, 11, 2079-2107.
11. Chen, D., Wang, X., & Zhao, J. (2012). Aircraft maintenance decision system based on real-time condition monitoring. Procedia Engineering, 29, 765-769.
12. Dinis, D., Barbosa-Póvoa, A., & Teixeira, Â. P. (2019). Valuing data in aircraft maintenance through big data analytics: A probabilistic approach for capacity planning using Bayesian networks. Computers & Industrial Engineering, 128, 920-936.
13. Durbhaka, G. K., & Selvaraj, B. (2016). Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach. 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI),
14. Eickemeyer, S. C., Borcherding, T., Schäfer, S., & Nyhuis, P. (2013). Validation of data fusion as a method for forecasting the regeneration workload for complex capital goods. Production Engineering, 7(2), 131-139.
15. Hesser, D. F., & Markert, B. (2019). Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks. Manufacturing letters, 19, 1-4.
16. Karthik, T., & Kamala, B. (2021). Cloud based AI approach for predictive maintenance and failure prevention. Journal of Physics: Conference Series,
17. Knotts, R. M. (1999). Civil aircraft maintenance and support Fault diagnosis from a business perspective. Journal of quality in maintenance engineering.
18. Koukaras, P., Dimara, A., Herrera, S., Zangrando, N., Krinidis, S., Ioannidis, D., Fraternali, P., Tjortjis, C., Anagnostopoulos, C.-N., & Tzovaras, D. (2022). Proactive Buildings: A Prescriptive Maintenance Approach. IFIP International Conference on Artificial Intelligence Applications and Innovations,
19. Kumar, U., Galar, D., Parida, A., Stenström, C., & Berges, L. (2013). Maintenance performance metrics: a state‐of‐the‐art review. Journal of quality in maintenance engineering.
20. Liu, P. D., & Wang, P. (2018). Some q-Rung Orthopair Fuzzy Aggregation Operators and their Applications to Multiple-Attribute Decision Making. International Journal of Intelligent Systems, 33(2), 259-280.
21. Marques, H., & Giacotto, A. (2019). Prescriptive maintenance: Building alternative plans for smart operations. FT2019. Proceedings of the 10th Aerospace Technology Congress, October 8-9, 2019, Stockholm, Sweden,
22. Meissner, R., Meyer, H., & Wicke, K. (2021). Concept and economic evaluation of prescriptive maintenance strategies for an automated condition monitoring system. International Journal of Prognostics and Health Management, 12(3).
23. Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., & Loncarski, J. (2018). Machine learning approach for predictive maintenance in industry 4.0. 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA),
24. Parida, A., & Chattopadhyay, G. (2007). Development of a multi‐criteria hierarchical framework for maintenance performance measurement (MPM). Journal of quality in maintenance engineering.
25. Paz, N. M., & Leigh, W. (1994). Maintenance scheduling: issues, results and research needs. International Journal of Operations & Production Management.
26. Pınar, A., Babak Daneshvar, R., & Özdemir, Y. S. (2021). q-Rung orthopair fuzzy TOPSIS method for green supplier selection problem. Sustainability, 13(2), 985.
27. Pinar, A., & Boran, F. E. (2020). A q-rung orthopair fuzzy multi-criteria group decision making method for supplier selection based on a novel distance measure. International Journal of Machine Learning and Cybernetics, 11(8), 1749-1780.
28. Rengasamy, D., Morvan, H. P., & Figueredo, G. P. (2018). Deep learning approaches to aircraft maintenance, repair and overhaul: A review. 2018 21st International Conference on Intelligent Transportation Systems (ITSC),
29. Samaranayake, P. (2006). Current practices and problem areas in aircraft maintenance planning and scheduling–interfaced/integrated system perspective. Proceedings of the 7th Asia Pacific Industrial Engineering and Management Systems Conference,
30. Samaranayake, P., & Kiridena, S. (2012). Aircraft maintenance planning and scheduling: an integrated framework. Journal of Quality in Maintenance Engineering.
31. Sriram, C., & Haghani, A. (2003). An optimization model for aircraft maintenance scheduling and re-assignment. Transportation Research Part A: Policy and Practice, 37(1), 29-48.
32. Syan, C. S., & Ramsoobag, G. (2019). Maintenance applications of multi-criteria optimization: A review. Reliability Engineering & System Safety, 190, 106520. "
33. Taghipour, A., Rouyendegh, B. D., Ünal, A., & Piya, S. (2022). Selection of suppliers for speech recognition products in IT projects by combining techniques with an integrated fuzzy MCDM. Sustainability, 14(3), 1777.
34. Van den Bergh, J., De Bruecker, P., Beliën, J., & Peeters, J. (2013). Aircraft maintenance operations: state of the art. HUB Research Paper 2013/09.
35. Yager, R. R. (2013). Pythagorean Membership Grades in Multicriteria Decision Making. Ieee Transactions on Fuzzy Systems, 22(4), 958-965.
36. Yager, R. R. (2016). Generalized orthopair fuzzy sets. Ieee Transactions on Fuzzy Systems, 25(5), 1222-1230.
37. Yager, R. R., & Alajlan, N. (2017). Approximate reasoning with generalized orthopair fuzzy sets. Information Fusion, 38, 65-73.
38. Zadeh, L. A. (1965). Fuzzy sets. Information control, 8(3), 338-353.