Artificial Intelligence–Driven Predictive Maintenance for Next-Generation Aircraft Systems
DOI:
https://doi.org/10.59675/E315Keywords:
Predictive maintenance; Artificial intelligence; Machine learning; Digital twin; Aircraft maintenance.Abstract
The aviation business encounters a major challenge with regard to the cost of aircraft maintenance, operational time, and assurance of safety in the era of rising complexity and demands of the fleet. The conventional reactive and preventive maintenance strategies have been unable to handle the more demanding needs of the modern aircraft systems that consist of advanced material, elaborate electronic systems as well as intricate assemblies of mechanisms. The paper will discuss the application and the performance of predictive maintenance technologies powered by artificial intelligence and specifically in the field of next generation aircraft systems, the use of machine learning to predict faults and the use of the digital twin technology. An analysis of the performance of the different predictive maintenance frameworks was done on several airplane platforms such as commercial aviation fleets and unmanned aerial vehicles. The study shows that machine learning models, especially ensemble algorithms of a random forest, gradient boosting machine, and deep neural network, have a higher accuracy of fault detection of more than 94% and lower false positive rates by about 67% than standard threshold-based monitoring systems. It was found that the implementation of digital twins allowed the real-time estimation of the state of a system with correlation coefficients over 0.92 between the expected and actual patterns of component degradation. Economic analysis indicates that predictive maintenance with the use of artificial intelligence lowers the number of unscheduled maintenance incidents by 48 per cent, reduces aircraft on-ground time by 35 per cent., and saves the company about 1.2 million dollars a year per plane due to optimized maintenance scheduling and improved component life. Commercial operator case studies show that predictive analytics can help to perform dedicated interventions that avoid disastrous failures but do not lead to preventable maintenance interventions, which helps achieve better safety margins and operational efficiency. It was detected that the combination of Internet of Things sensor networks with cloud-based analytics platforms allows scalable implementation in a wide variety of fleet configurations. Nonetheless, the issues associated with implementation such as data quality assurance, the requirements of model interpretability, and consideration of regulatory requirements demand cautiousness in the development, as well as implementation of the system. The results suggest that predictive maintenance based on artificial intelligence is a radical way of managing aviation systems, as it offers significant gains in safety, reliability, and economic operation as well as contributes to the development of autonomous maintenance decision-making algorithms in the future aviation systems.
References
Anderson DP, Williams JR. Evolution of aircraft maintenance practices in commercial aviation. J Air Transp Manag. 2018; 67:142-156.
Chen ML, Rodriguez SA. Advanced materials and system integration in modern aircraft design. Aerosp Sci Technol. 2019; 85:201-218.
Thompson KL. Complexity management in modern aircraft systems. IEEE Trans Aerosp Electron Syst. 2020;56(2):1247-1265.
Mitchell JA, Peterson DR. Component reliability analysis for next-generation aircraft. Reliab Eng Syst Saf. 2019; 182:305-321.
Kumar RS, Singh AP. Economic analysis of aircraft maintenance strategies. Int J Prod Econ. 2020; 219:177-194.
Wilson TR, Davis ML. Operating cost optimization in commercial aviation. Transp Res Part A Policy Pract. 2018; 118:342-358.
International Air Transport Association. Aircraft maintenance market analysis 2023. Montreal: IATA; 2023.
Jardine AK, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process. 2006;20(7):1483-1510. DOI: https://doi.org/10.1016/j.ymssp.2005.09.012
Mobley RK. An introduction to predictive maintenance. 2nd ed. New York: Elsevier; 2002. DOI: https://doi.org/10.1016/B978-075067531-4/50006-3
Ahmad R, Kamaruddin S. An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng. 2012;63(1):135-149. DOI: https://doi.org/10.1016/j.cie.2012.02.002
Goode KB, Moore J, Roylance BJ. Plant machinery working life prediction method utilizing reliability and condition-monitoring data. Proc Inst Mech Eng Part E J Process Mech Eng. 2000;214(2):109-122. DOI: https://doi.org/10.1243/0954408001530146
Heng A, Zhang S, Tan AC, Mathew J. Rotating machinery prognostics: state of the art, challenges and opportunities. Mech Syst Signal Process. 2009;23(3):724-739. DOI: https://doi.org/10.1016/j.ymssp.2008.06.009
Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D. Prognostics and health management design for rotary machinery systems: reviews, methodology and applications. Mech Syst Signal Process. 2014;42(1-2):314-334. DOI: https://doi.org/10.1016/j.ymssp.2013.06.004
Peng Y, Dong M, Zuo MJ. Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Technol. 2010;50(1-4):297-313. DOI: https://doi.org/10.1007/s00170-009-2482-0
Carvalho TP, Soares FA, Vita R, Francisco RP, Basto JP, Alcalá SG. A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng. 2019; 137:106024. DOI: https://doi.org/10.1016/j.cie.2019.106024
Zonta T, da Costa CA, da Rosa Righi R, de Lima MJ, da Trindade ES, Li GP. Predictive maintenance in the Industry 4.0: a systematic literature review. Comput Ind Eng. 2020; 150:106889. DOI: https://doi.org/10.1016/j.cie.2020.106889
Dalzochio J, Kunst R, Pignaton E, Binotto A, Sanyal S, Favilla J, et al. Machine learning and reasoning for predictive maintenance in Industry 4.0: current status and challenges. Comput Ind. 2020; 123:103298. DOI: https://doi.org/10.1016/j.compind.2020.103298
Lei Y, Li N, Guo L, Li N, Yan T, Lin J. Machinery health prognostics: a systematic review from data acquisition to RUL prediction. Mech Syst Signal Process. 2018; 104:799-834. DOI: https://doi.org/10.1016/j.ymssp.2017.11.016
Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX. Deep learning and its applications to machine health monitoring. Mech Syst Signal Process. 2019; 115:213-237. DOI: https://doi.org/10.1016/j.ymssp.2018.05.050
Chandola V, Banerjee A, Kumar V. Anomaly detection: a survey. ACM Comput Surv. 2009;41(3):1-58. DOI: https://doi.org/10.1145/1541880.1541882
Khan S, Yairi T. A review on the application of deep learning in system health management. Mech Syst Signal Process. 2018; 107:241-265. DOI: https://doi.org/10.1016/j.ymssp.2017.11.024
Tao F, Zhang H, Liu A, Nee AY. Digital twin in industry: state-of-the-art. IEEE Trans Ind Inform. 2019;15(4):2405-2415. DOI: https://doi.org/10.1109/TII.2018.2873186
Grieves M, Vickers J. Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen FJ, Flumerfelt S, Alves A, editors. Transdisciplinary perspectives on complex systems. Cham: Springer; 2017. p. 85-113. DOI: https://doi.org/10.1007/978-3-319-38756-7_4
Glaessgen E, Stargel D. The digital twin paradigm for future NASA and U.S. Air Force vehicles. In: Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference; 2012 Apr 23-26; Honolulu, HI. Reston: AIAA; 2012. DOI: https://doi.org/10.2514/6.2012-1818
Tuegel EJ, Ingraffea AR, Eason TG, Spottswood SM. Reengineering aircraft structural life prediction using a digital twin. Int J Aerosp Eng. 2011; 2011:154798. DOI: https://doi.org/10.1155/2011/154798
Lee J, Bagheri B, Kao HA. A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett. 2015; 3:18-23. DOI: https://doi.org/10.1016/j.mfglet.2014.12.001
Qi Q, Tao F. Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison. IEEE Access. 2018; 6:3585-3593. DOI: https://doi.org/10.1109/ACCESS.2018.2793265
Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge computing: vision and challenges. IEEE Internet Things J. 2016;3(5):637-646. DOI: https://doi.org/10.1109/JIOT.2016.2579198
Baptista M, Sankararaman S, de Medeiros IP, Nascimento C Jr, Prendinger H, Henriques EM. Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Comput Ind Eng. 2018; 115:41-53. DOI: https://doi.org/10.1016/j.cie.2017.10.033
Saxena A, Goebel K, Simon D, Eklund N. Damage propagation modeling for aircraft engine run-to-failure simulation. In: Proceedings of the 2008 International Conference on Prognostics and Health Management; 2008 Oct 6-9; Denver, CO. Piscataway: IEEE; 2008. DOI: https://doi.org/10.1109/PHM.2008.4711414
Ribeiro MT, Singh S, Guestrin C. Why should I trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13-17; San Francisco, CA. New York: ACM; 2016. p. 1135-1144. DOI: https://doi.org/10.1145/2939672.2939778
Haarman M, Mulders M, Vassiliadis C. Predictive maintenance 4.0: predict the unpredictable. Deloitte Netherlands; 2017.
Federal Aviation Administration. System safety handbook. Washington DC: FAA; 2000.
Wild TW, Kroes MJ. Aircraft powerplants. 8th ed. New York: McGraw-Hill; 2014.
Zhang W, Yang D, Wang H. Data-driven methods for predictive maintenance of industrial equipment: a survey. IEEE Syst J. 2019;13(3):2213-2227. DOI: https://doi.org/10.1109/JSYST.2019.2905565
Loutas TH, Roulias D, Pauly E, Kostopoulos V. The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mech Syst Signal Process. 2011;25(4):1339-1352. DOI: https://doi.org/10.1016/j.ymssp.2010.11.007
Breiman L. Random forests. Mach Learn. 2001;45(1):5-32. DOI: https://doi.org/10.1023/A:1010933404324
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735-1780. DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res. 2012; 13:281-305.
Boschert S, Rosen R. Digital twin: the simulation aspect. In: Hehenberger P, Bradley D, editors. Mechatronic futures: challenges and solutions for mechatronic systems and their designers. Cham: Springer; 2016. p. 59-74. DOI: https://doi.org/10.1007/978-3-319-32156-1_5
Volponi AJ. Gas turbine engine health management: past, present, and future trends. J Eng Gas Turbines Power. 2014;136(5):051201. DOI: https://doi.org/10.1115/1.4026126
Julier SJ, Uhlmann JK. Unscented filtering and nonlinear estimation. Proc IEEE. 2004;92(3):401-422. DOI: https://doi.org/10.1109/JPROC.2003.823141
Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, et al. A view of cloud computing. Commun ACM. 2010;53(4):50-58. DOI: https://doi.org/10.1145/1721654.1721672
Saxena A, Celaya J, Saha B, Saha S, Goebel K. Metrics for offline evaluation of prognostic performance. Int J Prognostics Health Manag. 2010;1(1):4-23. DOI: https://doi.org/10.36001/ijphm.2010.v1i1.1336
Mobley RK. Maintenance engineering handbook. 8th ed. New York: McGraw-Hill; 2014.
Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13-17; San Francisco, CA. New York: ACM; 2016. p. 785-794. DOI: https://doi.org/10.1145/2939672.2939785
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. DOI: https://doi.org/10.1038/nature14539
Simon D, Simon DL. Aircraft turbofan engine health estimation using constrained Kalman filtering. J Eng Gas Turbines Power. 2005;127(2):323-328. DOI: https://doi.org/10.1115/1.1789153
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