Artificial Intelligence for Aircraft Fault Detection
From Accuracy to Airworthiness through Modality-Aware Analysis, Validation, and Certification Perspectives
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Abstract
Artificial Intelligence (AI)-based fault detection has demonstrated high diagnostic accuracy in aircraft systems, with many studies reporting performance exceeding 90–99%. However, these results are predominantly obtained under controlled, offline, or simulation-based conditions, limiting their relevance for safety-critical aviation environments where failure consequences are severe and certification requirements are stringent. This gap highlights the need for evaluation frameworks that extend beyond accuracy to operational and airworthiness considerations. This survey presents a modality-aware analytical framework for AI-based aircraft fault detection, organizing existing methods along four key dimensions: input modality, data provenance, validation maturity, and model class. Using this taxonomy, this survey systematically reviews signal, image, log, and simulation-based approaches, demonstrating that reported performance is strongly influenced by data characteristics and evaluation conditions. The analysis shows that many high-performing models rely on low-maturity validation and lack evidence of generalization under real-world operating conditions. To address these limitations, this survey introduces an engineering-oriented deployment framework integrating modality-driven model selection, structured data preprocessing, multi-level validation, computational feasibility, and explainability. A deployment readiness model is further proposed to assess systems based on validation maturity, data coverage, interpretability, and operational constraints. This survey identifies key challenges, including the simulation-to-reality performance gap, lack of standardized benchmarks, limited adoption of explainable AI, and insufficient alignment with certification requirements, and outlines future research directions toward certification-aware AI design, lightweight deployable models, and multimodal digital twin integration.
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Ahmad, M. W., Akram, M. U., Ahmad, R., Hameed, K., & Hassan, A. (2022). Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights. ISA Transactions, 129, 355–371. https://doi.org/10.1016/j.isatra.2022.01.014 DOI: https://doi.org/10.1016/j.isatra.2022.01.014
Ahmed, W. (2025). Artificial intelligence in aviation: A review of machine learning and deep learning applications for enhanced safety and security. Premier Journal of Artificial Intelligence, 3(1), 100013. https://doi.org/10.70389/PJAI.100013 DOI: https://doi.org/10.70389/PJAI.100013
Azanaw, G. M. (2024). Integrating AI in Structural Health Monitoring (Shm): A Systematic Review on Advances, Challenges, and Future Directions. I-Manager’s Journal on Structural Engineering, 13(3). https://doi.org/10.26634/jste.13.3.21791 DOI: https://doi.org/10.26634/jste.13.3.21791
Bronz, M., Baskaya, E., Delahaye, D., & Puechmorel, S. (2020). Real-time fault detection on small fixed-wing UAVs using machine learning. DASC 2020 AIAA/IEEE 39th Digital Avionics Systems Conference, ISBN-978. https://doi.org/10.1109/DASC50938.2020.9256800 DOI: https://doi.org/10.1109/DASC50938.2020.9256800
Brunton, S. L., Kutz, J. N., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom-Schieber, A. W., Hogan, T., & McDonald, D. (2021). Data-driven aerospace engineering: Reframing the industry with machine learning. AIAA Journal, 59(8), 2820–2847. https://doi.org/10.2514/1.J060131 DOI: https://doi.org/10.2514/1.J060131
Ceken, S., & Tuncal, A. (2026). Artificial intelligence for sustainable aviation: a review on operational implementations and future perspectives. The Aeronautical Journal, 1–46. https://doi.org/10.1017/aer.2026.10138 DOI: https://doi.org/10.1017/aer.2026.10138
Chen, G., Yuan, J., Zhang, Y., Zhu, H., Huang, R., Wang, F., & Li, W. (2024). Enhancing reliability through interpretability: A comprehensive survey of interpretable intelligent fault diagnosis in rotating machinery. IEEE Access, 12, 103348–103379. https://doi.org/10.1109/ACCESS.2024.3430010 DOI: https://doi.org/10.1109/ACCESS.2024.3430010
Connolly, L., Garland, J., & Tobin, E. (2024). Deep-Learning-Based Defect Detection for Light Aircraft With Unmanned Aircraft Systems. IEEE Access, June. https://doi.org/10.1109/ACCESS.2024.3412204 DOI: https://doi.org/10.1109/ACCESS.2024.3412204
Dangut, M. D., Jennions, I. K., King, S., & Skaf, Z. (2023). A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach. Neural Computing and Applications, 35(4), 2991–3009. https://doi.org/10.1007/s00521-022-07167-8 DOI: https://doi.org/10.1007/s00521-022-07167-8
Dmitriev, K., Schumann, J., & Holzapfel, F. (2021). Toward certification of machine-learning systems for low criticality airborne applications. 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), 1–7. https://doi.org/10.1109/DASC52595.2021.9594467 DOI: https://doi.org/10.1109/DASC52595.2021.9594467
Donatus, R. E. (2025). Comprehensive Review of Artificial Intelligence for Image- and Signal-Based Nondestructive Testing in Aerospace Structures. Journal of Computers, Mechanical and Management, 4(6), 1–14. https://doi.org/10.57159/jcmm.4.6.25230.1 DOI: https://doi.org/10.57159/jcmm.4.6.25230
Donatus, R. E., Ubadike, O. C., Bonet, M. U., & Iyaghiba, S. D. (2025). Automated Aircraft Structural Defect Detection Using Deep Learning and Computer Vision. Mekatronika Journal of Mechatronics and Intelligent Manufacturing, 7(2), 108–123. https://doi.org/10.15282/mekatronika.v7i2.11787 DOI: https://doi.org/10.15282/mekatronika.v7i2.11787
Donatus, R. E., & Uhiah, L. O. (2026). Certification And Trustworthy Deployment of Explainable And Physics-Informed Artificial Intelligence For Aerospace Non-Destructive Testing. International Journal of Engineering Processing & Safety Research, 11(5), 1–30. https://doi.org/10.70382/caijepsr.v11i5.010 DOI: https://doi.org/10.70382/caijepsr.v11i5.010
Donatus, R., Uhiah, L., & Olanrewaju, A. (2025). Attention-Enhanced Temporal Modeling for Prognostics of Turbofan Engine Remaining Useful Life. FUDMA Journal of Sciences (FJS), 9(12), 581–589. https://doi.org/10.33003/fjs-2025-0912-4237
Du, W., Zhang, J., Meng, G., & Zhang, H. (2024). Aero-engine fault detection with an LSTM auto-encoder combined with a self-attention mechanism. Machines, 12(12), 879. https://doi.org/10.3390/machines12120879 DOI: https://doi.org/10.3390/machines12120879
Durand, J. G., Dubois, A., & Moss, R. J. (2023). Formal and Practical Elements for the Certification of Machine Learning Systems. AIAA/IEEE Digital Avionics Systems Conference - Proceedings. https://doi.org/10.1109/DASC58513.2023.10311201 DOI: https://doi.org/10.1109/DASC58513.2023.10311201
Ezhilarasu, C. M., Skaf, Z., & Jennions, I. K. (2021). A generalised methodology for the diagnosis of aircraft systems. Ieee Access, 9, 11437–11454. https://doi.org/10.1109/ACCESS.2021.3050877 DOI: https://doi.org/10.1109/ACCESS.2021.3050877
Huang, Y., Tao, J., Sun, G., Wu, T., Yu, L., & Zhao, X. (2023). A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis. Energy, 270, 126894. https://doi.org/10.1016/j.energy.2023.126894 DOI: https://doi.org/10.1016/j.energy.2023.126894
Jia, J., & Li, Y. (2023). Deep learning for structural health monitoring: Data, algorithms, applications, challenges, and trends. Sensors, 23(21), 8824. DOI: https://doi.org/10.3390/s23218824
Jia, L., Ezhilarasu, C. M., & Jennions, I. K. (2025). Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning. Applied Sciences, 15(6), 3232. https://doi.org/10.3390/app15063232 DOI: https://doi.org/10.3390/app15063232
Jia, Z., Li, Y., Wang, S., & Liu, Z. (2023). A multi-channel data-based fault diagnosis method integrating deep learning strategy for aircraft sensor system. Measurement Science and Technology, 34(2), 25115. https://doi.org/10.1088/1361-6501/aca219 DOI: https://doi.org/10.1088/1361-6501/aca219
Kaakai, F., Dmitriev, K., Adibhatla, S., Baskaya, E., Bezzecchi, E., Bharadwaj, R., Brown, B., Gentile, G., Gingins, C., & Grihon, S. (2022). Toward a machine learning development lifecycle for product certification and approval in aviation. SAE International Journal of Aerospace 339791, 15(2), 127–143. https://doi.org/10.4271/01-15-02-0009 DOI: https://doi.org/10.4271/01-15-02-0009
Kabashkin, I. (2024). Integration of Foundation Models and Federated Learning in AIoT-Based Aircraft Health Monitoring Systems. Mathematics, 12(21), 3428. https://doi.org/10.3390/math12213428 DOI: https://doi.org/10.3390/math12213428
Kabashkin, I. (2025). AI and Evolutionary Computation for Intelligent Aviation Health Monitoring. Electronics (2079-9292), 14(7). https://doi.org/10.3390/electronics14071369 DOI: https://doi.org/10.3390/electronics14071369
Kabashkin, I., Perekrestov, V., & Pivovar, M. (2025). AI-Driven Fault Detection and Maintenance Optimization for Aviation Technical Support Systems. Processes, 13(3), 666. https://doi.org/10.3390/pr13030666 DOI: https://doi.org/10.3390/pr13030666
Kefalas, M., Santiago Rojo Jr, J. de, Apostolidis, A., Van Den Herik, D., van Stein, B., & Bäck, T. (2022). Explainable artificial intelligence for exhaust gas temperature of turbofan engines. Journal of Aerospace Information Systems, 19(6), 447–454. DOI: https://doi.org/10.2514/1.I011058
Khanzada, H. R., Maqsood, A., & Basit, A. (2025). Artificial intelligence in UAV flight controls: deep reinforcement learning based altitude-hold strategies for fixed-wing UAVs. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3581966 DOI: https://doi.org/10.1109/ACCESS.2025.3581966
Kraemer, A. D., & Villani, E. (2024). Framework for Offline Data-Driven Aircraft Fault Diagnosis. Journal of Aerospace Information Systems, 21(4), 14. https://doi.org/10.2514/1.I011253 DOI: https://doi.org/10.2514/1.I011253
Kwakye, A. D., Jennions, I. K., & Ezhilarasu, C. M. (2024). Platform health management for aircraft maintenance–a review. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 238(3), 267–283. https://doi.org/10.1177/09544100231222516 DOI: https://doi.org/10.1177/09544100231219736
Lei, B., Shen, N., Ji, D., Ouyang, R., Yang, L., Liu, Q., Gao, B., & Wang, Z. (2024). A comprehensive review of key technologies for enhancing the reliability of aero-engines. IEEE Access, 12, 148315–148331. https://doi.org/10.1109/ACCESS.2024.3474737 DOI: https://doi.org/10.1109/ACCESS.2024.3474737
Li, J., King, S., & Jennions, I. (2023). Intelligent fault diagnosis of an aircraft fuel system using machine learning—A literature review. Machines, 11(4), 481. https://doi.org/10.3390/machines11040481 DOI: https://doi.org/10.3390/machines11040481
Li, J., King, S., & Jennions, I. (2025). Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System. Algorithms, 18(2), 73. https://doi.org/https://doi.org/10.3390/a18020073 DOI: https://doi.org/10.3390/a18020073
Li, M., Zhu, L., Luo, M., & Ke, T. (2025). Remaining Useful Life Prediction of Airplane Engine Based on Bidirectional Mamba and Causal Discovery. Sensors, 25(11), 3429. https://doi.org/10.3390/s25113429 DOI: https://doi.org/10.3390/s25113429
Li, Z., Ma, J., Fan, R., Zhao, Y., Ai, J., & Dong, Y. (2025). Aircraft Sensor Fault Diagnosis Based on GraphSage and Attention Mechanism. Sensors (Basel, Switzerland), 25(3), 809. https://doi.org/10.3390/s25030809 DOI: https://doi.org/10.3390/s25030809
Li, Z., Zhang, Y., Ai, J., Zhao, Y., Yu, Y., & Dong, Y. (2023). A lightweight and explainable data-driven scheme for fault detection of aerospace sensors. IEEE Transactions on Aerospace and Electronic Systems, 59(6), 8392–8410. https://doi.org/10.1109/TAES.2023.3303855 DOI: https://doi.org/10.1109/TAES.2023.3303855
Liu, Y., Dong, J., Li, Y., Gong, X., & Wang, J. (2022). A UAV-based aircraft surface defect inspection system via external constraints and deep learning. IEEE Transactions on Instrumentation and Measurement, 71, 1–15. https://doi.org/10.1109/TIM.2022.3198713 DOI: https://doi.org/10.1109/TIM.2022.3198713
Luettig, B., Akhiat, Y., & Daw, Z. (2024). ML meets aerospace: challenges of certifying airborne AI. Frontiers in Aerospace Engineering, 3, 1475139. https://doi.org/10.3389/fpace.2024.1475139 DOI: https://doi.org/10.3389/fpace.2024.1475139
Ofoegbu, E., & Josiah, P. (2026). A review of artificial intelligence applications for fault detection in aerospace hydraulics systems. Australian Journal of Multi-Disciplinary Engineering, 1–17. https://doi.org/10.1080/14488388.2026.2621623 DOI: https://doi.org/10.1080/14488388.2026.2621623
Plevris, V., & Papazafeiropoulos, G. (2024). AI in structural health monitoring for infrastructure maintenance and safety. Infrastructures, 9(12), 225. https://doi.org/10.3390/infrastructures9120225 DOI: https://doi.org/10.3390/infrastructures9120225
Shafi, I., Mazhar, M. F., Fatima, A., Alvarez, R. M., Miró, Y., Espinosa, J. C. M., & Ashraf, I. (2023). Deep learning-based real time defect detection for optimization of aircraft manufacturing and control performance. Drones, 7(1), 31. https://doi.org/10.3390/drones7010031 DOI: https://doi.org/10.3390/drones7010031
Shen, Y., & Khorasani, K. (2020). Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines. Neural Networks, 130, 126–142. https://doi.org/10.1016/j.neunet.2020.07.001 DOI: https://doi.org/10.1016/j.neunet.2020.07.001
Sridhar, C., Gupta, V., Jain, P., & Vaidhyanathan, K. (2025). Approach towards semi-automated certification of low criticality ML-enabled airborne applications. 2025 IEEE/ACM 4th International Conference on AI Engineering–Software Engineering for AI (CAIN), 107–112. DOI: https://doi.org/10.1109/CAIN66642.2025.00020
Tsallis, C., Papageorgas, P., Piromalis, D., & Munteanu, R. A. (2025). Application-wise review of Machine Learning-based predictive maintenance: Trends, challenges, and future directions. Applied Sciences, 15(9), 4898. https://doi.org/10.3390/app15094898 DOI: https://doi.org/10.3390/app15094898
Wu, J., Kong, L., Kang, S., Zuo, H., Yang, Y., & Cheng, Z. (2024). Aircraft engine fault diagnosis model based on 1DCNN-BiLSTM with CBAM. Sensors, 24(3), 780. https://doi.org/10.3390/s24030780 DOI: https://doi.org/10.3390/s24030780
Young, L. K., Hussain, K., & Oh, I.-Y. (2025). Validation for Predictive Maintenance of Aircraft Systems Using AI Models Developed With Rotor Blade Vibration Data. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3550822 DOI: https://doi.org/10.1109/ACCESS.2025.3550822
Zhang, W., Liu, Z., Jia, Z., Wang, X., Yan, W., & Wang, K. (2025). Application of a multimodal deep learning model based on recursive fusion feature map with transformer-TCN for complex fault diagnosis of flying wing UAV actuators. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2025.3561438 DOI: https://doi.org/10.1109/TIM.2025.3561438
Zhao, Y.-P., & Chen, Y.-B. (2022). Extreme learning machine based transfer learning for aero engine fault diagnosis. Aerospace Science and Technology, 121, 107311. https://doi.org/10.1016/j.ast.2021.107311 DOI: https://doi.org/10.1016/j.ast.2021.107311
Zhao, Y., Zhao, H., Ai, J., & Dong, Y. (2022). Robust Data‐Driven Fault Detection: An Application to Aircraft Air Data Sensors. International Journal of Aerospace Engineering, 2022(1), 2918458. DOI: https://doi.org/10.1155/2022/2918458
Zolghadri, A. (2024). A review of fault management issues in aircraft systems: Current status and future directions. Progress in Aerospace Sciences, 147, 101008. https://doi.org/10.1016/j.paerosci.2024.101123 DOI: https://doi.org/10.1016/j.paerosci.2024.101008
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