Artificial Intelligence Application on Aircraft Maintenance: A Systematic Literature Review
DOI:
https://doi.org/10.4108/eetiot.6938Keywords:
Artificial intelligence, Aircraft maintenance, Industry 5.0Abstract
Maintenance is an essential aspect of supporting aircraft operations. However, there are still several obstacles and challenges in the process, such as incomplete technical record data, irregular maintenance schedules, unscheduled component replacement, unavailability of tools or components, recurring problems, and a long time for troubleshooting. Digitalization and the massive use of artificial intelligence (AI) in various sectors have been widely carried out in the industry 5.0 era today, especially in the aviation industry. It offers several advantages to optimize aircraft maintenance and operations, such as predictive maintenance, fault detection, failure diagnosis, and intelligent monitoring systems. The utilization of AI has the potential to solve obstacles and challenges in aircraft maintenance activities, such as improving aircraft reliability, reducing aircraft downtime, improving safety, and reducing maintenance costs. This research uses the Systematic Literature Review method, which aims to review and provide an understanding of objectives, strategies, methods, and equipment objects involved in the application of AI in aircraft maintenance and repair scope. The findings and understanding from this research can be used as a basis for utilizing or adopting AI in aircraft maintenance to be more targeted and efficient in the future. This study reviews and presents research trends from reputable journals and proceedings screened using a unique protocol.
Downloads
References
[1] Badan Pusat Statistik: Statistik Transportasi Udara 2018., Jakarta (2019)
[2] katadata.co.id: Kecelakaan pesawat di Indonesia, https://databoks.katadata.co.id/datapublish/2022/03/22/hampir-tiap-tahun-ada-kecelakaan-pesawat-di-indonesia-ini-datanya
[3] Muhammad Fransyah: Human Factor Analysis Using the EEAM Method From Investigation Reports of Aircraft Accidents Related To The Maintenance Side, (2023)
[4] Wild, G.: A Quantitative Study of Aircraft Maintenance Accidents in Commercial Air Transport. Aerospace. 10, 689 (2023). https://doi.org/10.3390/aerospace10080689 DOI: https://doi.org/10.3390/aerospace10080689
[5] Pati, D., Lorusso, L.N.: How to Write a Systematic Review of the Literature. HERD: Health Environments Research & Design Journal. 11, 15–30 (2018). https://doi.org/10.1177/1937586717747384 DOI: https://doi.org/10.1177/1937586717747384
[6] Carrera-Rivera, A., Ochoa, W., Larrinaga, F., Lasa, G.: How to conduct a systematic literature review: A quick guide for computer science research. MethodsX. 9, 101895 (2022). https://doi.org/10.1016/j.mex.2022.101895 DOI: https://doi.org/10.1016/j.mex.2022.101895
[7] Luke Pittway: Systematic literature reviews. The SAGE dictionary of qualitative management research. 216–2018 (2008)
[8] Xiao, Y., Watson, M.: Guidance on Conducting a Systematic Literature Review, (2019)
[9] Li, J.-H., Gao, X.-Y., Lu, X., Liu, G.-D.: Multi-Head Attention-Based Hybrid Deep Neural Network for Aeroengine Risk Assessment. IEEE Access. 11, 113376–113389 (2023). https://doi.org/10.1109/ACCESS.2023.3323843 DOI: https://doi.org/10.1109/ACCESS.2023.3323843
[10] Ullah, S., Li, S., Khan, K., Khan, S., Khan, I., Eldin, S.M.: An Investigation of Exhaust Gas Temperature of Aircraft Engine Using LSTM. IEEE Access. 11, 5168–5177 (2023). https://doi.org/10.1109/ACCESS.2023.3235619 DOI: https://doi.org/10.1109/ACCESS.2023.3235619
[11] Thakkar, U., Chaoui, H.: Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks. Actuators. 11, (2022). https://doi.org/10.3390/act11030067 DOI: https://doi.org/10.3390/act11030067
[12] Apostolidis, A., Bouriquet, N., Stamoulis, K.P.: AI-Based Exhaust Gas Temperature Prediction for Trustworthy Safety-Critical Applications. Aerospace. 9, (2022). https://doi.org/10.3390/aerospace9110722 DOI: https://doi.org/10.3390/aerospace9110722
[13] de Pater, I., Reijns, A., Mitici, M.: Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics. Reliab Eng Syst Saf. 221, (2022). https://doi.org/10.1016/j.ress.2022.108341 DOI: https://doi.org/10.1016/j.ress.2022.108341
[14] Hermawan, A.P., Kim, D.-S., Lee, J.-M.: Predictive Maintenance of Aircraft Engine using Deep Learning Technique. In: 2020 International Conference on Information and Communication Technology Convergence (ICTC). pp. 1296–1298. IEEE (2020) DOI: https://doi.org/10.1109/ICTC49870.2020.9289466
[15] Liu, L., Wang, L., Yu, Z.: Remaining Useful Life Estimation of Aircraft Engines Based on Deep Convolution Neural Network and LightGBM Combination Model. International Journal of Computational Intelligence Systems. 14, (2021). https://doi.org/10.1007/s44196-021-00020-1 DOI: https://doi.org/10.1007/s44196-021-00020-1
[16] Boujamza, A., Lissane Elhaq, S.: Attention-based LSTM for Remaining Useful Life Estimation of Aircraft Engines. In: IFAC-PapersOnLine. pp. 450–455. Elsevier B.V. (2022) DOI: https://doi.org/10.1016/j.ifacol.2022.07.353
[17] Berghout, T., Mouss, L.H., Kadri, O., Saïdi, L., Benbouzid, M.: Aircraft engines remaining useful life prediction with an improved online sequential extreme learning machine. Applied Sciences (Switzerland). 10, (2020). https://doi.org/10.3390/app10031062 DOI: https://doi.org/10.3390/app10031062
[18] Kulagin, V.P., Akimov, D.A., Pavelyev, S.A., Potapov, D.A.: Automated Identification of Critical Malfunctions of Aircraft Engines Based on Modified Wavelet Transform and Deep Neural Network Clustering. In: IOP Conference Series: Materials Science and Engineering. Institute of Physics Publishing (2020) DOI: https://doi.org/10.1088/1757-899X/714/1/012014
[19] Liu, X., Xiong, L., Zhang, Y., Luo, C.: Remaining Useful Life Prediction for Turbofan Engine Using SAE-TCN Model †. Aerospace. 10, (2023). https://doi.org/10.3390/aerospace10080715 DOI: https://doi.org/10.3390/aerospace10080715
[20] Asif, O., Haider, S.A.L.I., Naqvi, S.R., Zaki, J.F.W., Kwak, K.S., Islam, S.M.R.: A Deep Learning Model for Remaining Useful Life Prediction of Aircraft Turbofan Engine on C-MAPSS Dataset. IEEE Access. 10, 95425–95440 (2022). https://doi.org/10.1109/ACCESS.2022.3203406 DOI: https://doi.org/10.1109/ACCESS.2022.3203406
[21] Lee, J., Mitici, M.: Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics. Reliab Eng Syst Saf. 230, (2023). https://doi.org/10.1016/j.ress.2022.108908 DOI: https://doi.org/10.1016/j.ress.2022.108908
[22] Listou Ellefsen, A., Bjørlykhaug, E., Æsøy, V., Ushakov, S., Zhang, H.: Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture. Reliab Eng Syst Saf. 183, 240–251 (2019). https://doi.org/10.1016/j.ress.2018.11.027 DOI: https://doi.org/10.1016/j.ress.2018.11.027
[23] Zheng, C., Liu, W., Chen, B., Gao, D., Cheng, Y., Yang, Y., Zhang, X., Li, S., Huang, Z., Peng, J.: A Data-driven Approach for Remaining Useful Life Prediction of Aircraft Engines. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). pp. 184–189. IEEE (2018) DOI: https://doi.org/10.1109/ITSC.2018.8569915
[24] Zhu, X.B.: Aviation Rivet Classification and Anomaly Detection Based on Deep Learning. International Journal of Aerospace Engineering. 2023, (2023). https://doi.org/10.1155/2023/3546838 DOI: https://doi.org/10.1155/2023/3546838
[25] Zhou, D., Zhuang, X., Zuo, H., Wang, H., Yan, H.: Deep Learning-Based Approach for Civil Aircraft Hazard Identification and Prediction. IEEE Access. 8, 103665–103683 (2020). https://doi.org/10.1109/ACCESS.2020.2997371 DOI: https://doi.org/10.1109/ACCESS.2020.2997371
[26] Dangut, M.D.: Rescaled-LSTM for Predicting Aircraft Component Replacement Under Imbalanced Dataset Constraint. (2020)
[27] Hsu, T.H., Chang, Y.J., Hsu, H.K., Chen, T.T., Hwang, P.W.: Predicting the Remaining Useful Life of Landing Gear with Prognostics and Health Management (PHM). Aerospace. 9, (2022). https://doi.org/10.3390/aerospace9080462 DOI: https://doi.org/10.3390/aerospace9080462
[28] Rosero, R.L., Silva, C., Ribeiro, B.: Remaining Useful Life Estimation of Cooling Units via Time-Frequency Health Indicators with Machine Learning. Aerospace. 9, (2022). https://doi.org/10.3390/aerospace9060309 DOI: https://doi.org/10.3390/aerospace9060309
[29] Abdelghany, E.S., Farghaly, M.B., Almalki, M.M., Sarhan, H.H., Essa, M.E.S.M.: Machine Learning and IoT Trends for Intelligent Prediction of Aircraft Wing Anti-Icing System Temperature. Aerospace. 10, (2023). https://doi.org/10.3390/aerospace10080676 DOI: https://doi.org/10.3390/aerospace10080676
[30] Li, W., Hou, N.: Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model. Sci Program. 2022, (2022). https://doi.org/10.1155/2022/8455629 DOI: https://doi.org/10.1155/2022/8455629
[31] Brandoli, B., de Geus, A.R., Souza, J.R., Spadon, G., Soares, A., Rodrigues, J.F., Komorowski, J., Matwin, S.: Aircraft fuselage corrosion detection using artificial intelligence. Sensors. 21, (2021). https://doi.org/10.3390/s21124026 DOI: https://doi.org/10.3390/s21124026
[32] Chen, J., Chen, S., Ma, C., Jing, Z., Xu, Q.: Fault Detection of Aircraft Control System Based on Negative Selection Algorithm. International Journal of Aerospace Engineering. 2020, (2020). https://doi.org/10.1155/2020/8833825 DOI: https://doi.org/10.1155/2020/8833825
[33] Liu, X., Liu, L., Liu, D., Wang, L., Guo, Q., Peng, X.: A Hybrid Method of Remaining Useful Life Prediction for Aircraft Auxiliary Power Unit. IEEE Sens J. 20, 7848–7858 (2020). https://doi.org/10.1109/JSEN.2020.2979797 DOI: https://doi.org/10.1109/JSEN.2020.2979797
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
