{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T17:02:41Z","timestamp":1779296561285,"version":"3.51.4"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T00:00:00Z","timestamp":1763856000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":36,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The increasing reliance on Unmanned Aerial Vehicles (UAVs) across critical industries\u2014including defense, logistics, and infrastructure inspection\u2014demands robust and accurate fault diagnosis systems to ensure operational safety and efficiency. However, the integration of Artificial Intelligence (AI) in UAV fault detection and predictive maintenance raises significant legal and regulatory concerns, particularly regarding liability, accountability, and transparency. In this study, it is aimed to give a better understanding of the co-founding domains of Explainable AI (XAI) and legal framework in addressing the issues of fault diagnosis of autonomous UAV systems. It investigates the legal conflicts that may arise from aviation safety compliance regarding the reliability of black-box-like AI models used for the detection of drone faults, and the study argues why interpretable AI is a must-have for compliance with regulatory authorities and courtroom verdicts. The liability attribution in UAV failures is further discussed to assess whether responsibility lies with manufacturers, software developers, or end-users in cases of AI-induced malfunctions. By examining current aviation safety laws, data protection policies, and ethical AI guidelines, the work proposes a framework that integrates transparent AI methodologies to ensure legal compliance while enhancing UAV reliability. The findings highlight that XAI-driven fault diagnosis improves safety and maintenance protocols while playing a crucial role in mitigating perhaps legal risks and fostering supposedly trust in AI-powered UAV operations.<\/jats:p>","DOI":"10.1007\/s44163-025-00690-2","type":"journal-article","created":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T10:14:24Z","timestamp":1763892864000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Legal accountability and UAV fault diagnosis explainable AI in aviation safety and regulatory compliance for liability challenges"],"prefix":"10.1007","volume":"5","author":[{"given":"Tameem Hadi","family":"Fadhil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luttfi A.","family":"Al-Haddad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mustafa I.","family":"Al-Karkhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,23]]},"reference":[{"key":"690_CR1","doi-asserted-by":"publisher","first-page":"221","DOI":"10.12720\/jcm.20.2.221-228","volume":"20","author":"T Hadi Fadhil","year":"2025","unstructured":"Hadi Fadhil T, Al-Karkhi MI, Al-Haddad LA. Legal and communication challenges in smart grid cybersecurity classification of network resilience under cyber attacks using machine learning. J Commun. 2025;20:221\u20138.","journal-title":"J Commun"},{"key":"690_CR2","doi-asserted-by":"publisher","first-page":"21","DOI":"10.12913\/22998624\/201346","volume":"19","author":"LA Al-Haddad","year":"2025","unstructured":"Al-Haddad LA, \u0141ukaszewicz A, Majdi HSh, et al. Energy consumption and efficiency degradation predictive analysis in unmanned aerial vehicle batteries using deep neural networks. Adv Sci Technol Res J. 2025;19:21\u201330.","journal-title":"Adv Sci Technol Res J"},{"key":"690_CR3","doi-asserted-by":"publisher","first-page":"465","DOI":"10.17509\/ajse.v5i2.89023","volume":"5","author":"A-RK Jweri","year":"2025","unstructured":"Jweri A-RK, Ogaili AAF, Amin SA, et al. Enhancing predictive maintenance in energy systems using a hybrid Kolmogorov-Arnold network (KAN) with short-time Fourier transform (STFT) framework for rotating machinery. ASEAN J Sci Eng. 2025;5:465\u201394.","journal-title":"ASEAN J Sci Eng"},{"key":"690_CR4","doi-asserted-by":"publisher","DOI":"10.3390\/machines13050401","author":"ST Bunyan","year":"2025","unstructured":"Bunyan ST, Khan ZH, Al-Haddad LA, et al. Intelligent thermal condition monitoring for predictive maintenance of gas turbines using machine learning. Machines. 2025. https:\/\/doi.org\/10.3390\/machines13050401.","journal-title":"Machines"},{"key":"690_CR5","doi-asserted-by":"publisher","first-page":"106872","DOI":"10.1016\/j.rineng.2025.106872","volume":"27","author":"AS Abdul-Zahra","year":"2025","unstructured":"Abdul-Zahra AS, Al-Haddad LA, Abdulsahib IA, et al. Sustainable thermal load prediction in residential buildings using machine learning: A case study analysis. Result Eng. 2025;27:106872.","journal-title":"Result Eng"},{"key":"690_CR6","doi-asserted-by":"crossref","unstructured":"Khan ZH, Mekid S, Al-Haddad LA, Jaber AA, AI Enabled Manufacturing: A Deep Learning Approach to Network Fault Detection. In: Proceedings of 2025 4th International Conference on Computing and Information Technology, ICCIT 2025. pp 245\u2013250","DOI":"10.1109\/ICCIT63348.2025.10989388"},{"key":"690_CR7","doi-asserted-by":"publisher","first-page":"35995","DOI":"10.1038\/s41598-025-19728-7","volume":"15","author":"SJ Hamandi","year":"2025","unstructured":"Hamandi SJ, Al-Haddad LA, Shaaban SM, Flah A. Child behavior recognition in social robot interaction using stacked deep neural networks and biomechanical signals. Sci Rep. 2025;15:35995. https:\/\/doi.org\/10.1038\/s41598-025-19728-7.","journal-title":"Sci Rep"},{"key":"690_CR8","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s44163-025-00582-5","volume":"5","author":"WK Jawad","year":"2025","unstructured":"Jawad WK, Al-Haddad LA. Stacked temporal deep learning for early-stage degradation forecasting in lithium-metal batteries. Discover Artif Intell. 2025;5:295. https:\/\/doi.org\/10.1007\/s44163-025-00582-5.","journal-title":"Discover Artif Intell"},{"key":"690_CR9","doi-asserted-by":"publisher","first-page":"8","DOI":"10.64071\/3080-5724.1021","volume":"1","author":"FA Hashim","year":"2025","unstructured":"Hashim FA, Mohialden YM, Hussien NM. Hybrid Feature Selection and Ensemble Classification for Climate Change Indicators: a machine learning approach. Terra Joule J. 2025;1:8.","journal-title":"Terra Joule J"},{"key":"690_CR10","first-page":"3","volume":"1","author":"MI Al-Karkhi","year":"2024","unstructured":"Al-Karkhi MI, Rzadkowski G, Ibraheem L, Aqib M. Anomaly detection in electrical systems using machine learning and statistical analysis. Terra Joule J. 2024;1:3.","journal-title":"Terra Joule J"},{"key":"690_CR11","first-page":"1","volume":"1","author":"LA Al-Haddad","year":"2024","unstructured":"Al-Haddad LA, Kahachi HAH, Ur Rehman HZ, et al. Advancing sustainability in buildings using an integrated aerodynamic fa\u00e7ade: potential of artificial intelligence. Terra Joule J. 2024;1:1.","journal-title":"Terra Joule J"},{"key":"690_CR12","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/s13344-025-0041-6","volume":"39","author":"LA Al-Haddad","year":"2025","unstructured":"Al-Haddad LA, Fattah MY, Al-Soudani WHS, et al. Enhanced load-settlement curve forecasts for open-ended pipe piles incorporating soil plug constraints using shallow and deep neural networks. China Ocean Eng. 2025;39:562\u201372. https:\/\/doi.org\/10.1007\/s13344-025-0041-6.","journal-title":"China Ocean Eng"},{"key":"690_CR13","doi-asserted-by":"publisher","DOI":"10.1515\/eng-2024-0052","author":"WH Alawee","year":"2024","unstructured":"Alawee WH, Al-Haddad LA, Basem A, Al-Haddad AA. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks. Open Eng. 2024. https:\/\/doi.org\/10.1515\/eng-2024-0052.","journal-title":"Open Eng"},{"key":"690_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s13762-024-05784-5","author":"SA Al-Haddad","year":"2024","unstructured":"Al-Haddad SA, Al-Haddad LA, Jaber AA. Environmental engineering solutions for efficient soil classification in southern Syria: a clustering-correlation extreme learning approach. Int J Environ Sci Technol. 2024. https:\/\/doi.org\/10.1007\/s13762-024-05784-5.","journal-title":"Int J Environ Sci Technol"},{"key":"690_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109241","author":"AA Al-Haddad","year":"2024","unstructured":"Al-Haddad AA, Al-Haddad LA, Al-Haddad SA, et al. Towards dental diagnostic systems: Synergizing wavelet transform with generative adversarial networks for enhanced image data fusion. Comput Biol Med. 2024. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.109241.","journal-title":"Comput Biol Med"},{"key":"690_CR16","doi-asserted-by":"publisher","first-page":"66","DOI":"10.2478\/joeb-2023-0009","volume":"14","author":"WH Alawee","year":"2023","unstructured":"Alawee WH, Basem A, Al-Haddad LA. Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis. J Electr Bioimpedance. 2023;14:66\u201372.","journal-title":"J Electr Bioimpedance"},{"key":"690_CR17","doi-asserted-by":"publisher","first-page":"56","DOI":"10.15587\/1729-4061.2022.263624","volume":"4","author":"AR Hassan","year":"2022","unstructured":"Hassan AR. Effects of rotational speed on the natural frequency of the differential bevel gear. Eastern-Europ J Enterprise Technol. 2022;4:56\u201363.","journal-title":"Eastern-Europ J Enterprise Technol"},{"key":"690_CR18","first-page":"160","volume":"4","author":"AS Hadi","year":"2025","unstructured":"Hadi AS, Al-Haddad LA. Towards fault diagnosis interpretability: gradient boosting framework for vibration-based detection of experimental gear failures. J Dynam Monitor Diagnost. 2025;4:160\u20139.","journal-title":"J Dynam Monitor Diagnost"},{"key":"690_CR19","unstructured":"Al-Haddad LA, Jaber A (2022) Applications of machine learning techniques for fault diagnosis of UAVs. In: CEUR workshop proceedings. 2022. pp 19\u201325"},{"key":"690_CR20","doi-asserted-by":"publisher","first-page":"9305","DOI":"10.1007\/s13369-021-05522-w","volume":"46","author":"SY Wong","year":"2021","unstructured":"Wong SY, Choe CWC, Goh HH, et al. Power transmission line fault detection and diagnosis based on artificial intelligence approach and its development in UAV: a review. Arab J Sci Eng. 2021;46:9305\u201331. https:\/\/doi.org\/10.1007\/s13369-021-05522-w.","journal-title":"Arab J Sci Eng"},{"key":"690_CR21","doi-asserted-by":"crossref","unstructured":"Baskaya E, Bronz M, Delahaye D, Fault detection & diagnosis for small UAVs via machine learning. In: 2017 IEEE\/AIAA 36th Digital Avionics Systems Conference (DASC). IEEE, 2017. pp 1\u20136","DOI":"10.1109\/DASC.2017.8102037"},{"key":"690_CR22","doi-asserted-by":"publisher","unstructured":"Al-Haddad LA, Giernacki W, Shandookh AA, et al, Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis? Eksploatacja i Niezawodno\u015b\u0107\u2014Maintenance and Reliability. 2023. https:\/\/doi.org\/10.17531\/ein\/176318","DOI":"10.17531\/ein\/176318"},{"key":"690_CR23","doi-asserted-by":"crossref","unstructured":"Mashhadi MJ, Hemmati H, Hybrid deep neural networks to infer state models of black-box systems. In: Proceedings of the 35th IEEE\/ACM International Conference on Automated Software Engineering.2020. pp 299\u2013311","DOI":"10.1145\/3324884.3416559"},{"key":"690_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/s23156879","author":"B Alvey","year":"2023","unstructured":"Alvey B, Anderson D, Keller J, Buck A. Linguistic explanations of black box deep learning detectors on simulated aerial drone imagery. Sensors. 2023. https:\/\/doi.org\/10.3390\/s23156879.","journal-title":"Sensors"},{"key":"690_CR25","doi-asserted-by":"crossref","unstructured":"Luo Q, Li W, Zhuge J, Shao J, A method for constructing an aerial image dataset of civil aircraft \u201cBlack Box\u201d Using AIGC-Generated samples. In: 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA). 2024; pp 181\u2013185","DOI":"10.1109\/ICMLCA63499.2024.10754398"},{"key":"690_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/rs12193112","author":"M Hatfield","year":"2020","unstructured":"Hatfield M, Cahill C, Webley P, et al. Integration of unmanned aircraft systems into the national airspace system-efforts by the University of Alaska to support the FAA\/NASA UAS traffic management program. Remote Sens (Basel). 2020. https:\/\/doi.org\/10.3390\/rs12193112.","journal-title":"Remote Sens (Basel)"},{"key":"690_CR27","first-page":"22","volume":"21","author":"CD Love","year":"2015","unstructured":"Love CD, Lawson ST, Holton AE. News from above: first amendment implications of the Federal Aviation Administration ban on commercial drones. BUJ Sci & Tech L. 2015;21:22.","journal-title":"BUJ Sci & Tech L"},{"key":"690_CR28","first-page":"34","volume":"4","author":"TM Ravich","year":"2019","unstructured":"Ravich TM. Emerging technologies and enforcement problems: the federal aviation administration and drones as a case study. Loy U Chi J Regul Compl. 2019;4:34.","journal-title":"Loy U Chi J Regul Compl"},{"key":"690_CR29","first-page":"425","volume":"49","author":"T Kwon","year":"2021","unstructured":"Kwon T, Nah S, Jeon S. An understanding of the legal framework of EASA UAS regulation towards improvement of aviation safety law. J Korean Soc Aeronaut Space Sci. 2021;49:425\u201335.","journal-title":"J Korean Soc Aeronaut Space Sci"},{"key":"690_CR30","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/B978-0-323-91940-1.00012-8","volume-title":"Unmanned Aerial Systems in Agriculture","author":"A Rauhala","year":"2023","unstructured":"Rauhala A, Tuomela A, Levi\u00e4kangas P. Chapter 12 - An overview of unmanned aircraft systems (UAS) governance and regulatory frameworks in the European Union (EU). In: Bochtis D, Tagarakis AC, Kateris D, editors. Unmanned Aerial Systems in Agriculture. Academic Press; 2023. p. 269\u201385."},{"key":"690_CR31","doi-asserted-by":"publisher","first-page":"101281","DOI":"10.1016\/j.iot.2024.101281","volume":"27","author":"F Tlili","year":"2024","unstructured":"Tlili F, Ayed S, Chaari Fourati L. Advancing UAV security with artificial intelligence: a comprehensive survey of techniques and future directions. Int Things. 2024;27:101281. https:\/\/doi.org\/10.1016\/j.iot.2024.101281.","journal-title":"Int Things"},{"key":"690_CR32","doi-asserted-by":"publisher","first-page":"100361","DOI":"10.1016\/j.array.2024.100361","volume":"23","author":"AVR Katkuri","year":"2024","unstructured":"Katkuri AVR, Madan H, Khatri N, et al. Autonomous UAV navigation using deep learning-based computer vision frameworks: a systematic literature review. Array. 2024;23:100361. https:\/\/doi.org\/10.1016\/j.array.2024.100361.","journal-title":"Array"},{"key":"690_CR33","doi-asserted-by":"publisher","first-page":"100590","DOI":"10.1016\/j.ijft.2024.100590","volume":"21","author":"M Al Radi","year":"2024","unstructured":"Al Radi M, AlMallahi MN, Al-Sumaiti AS, et al. Progress in artificial intelligence-based visual servoing of autonomous unmanned aerial vehicles (UAVs). Int J Thermofluids. 2024;21:100590. https:\/\/doi.org\/10.1016\/j.ijft.2024.100590.","journal-title":"Int J Thermofluids"},{"key":"690_CR34","doi-asserted-by":"publisher","first-page":"105557","DOI":"10.1016\/j.conengprac.2023.105557","volume":"137","author":"K Ahmadi","year":"2023","unstructured":"Ahmadi K, Asadi D, Merheb A, et al. Active fault-tolerant control of quadrotor UAVs with nonlinear observer-based sliding mode control validated through hardware in the loop experiments. Control Eng Pract. 2023;137:105557. https:\/\/doi.org\/10.1016\/j.conengprac.2023.105557.","journal-title":"Control Eng Pract"},{"key":"690_CR35","doi-asserted-by":"publisher","first-page":"108322","DOI":"10.1016\/j.ast.2023.108322","volume":"138","author":"W Gong","year":"2023","unstructured":"Gong W, Li B, Ahn CK, Yang Y. Prescribed-time extended state observer and prescribed performance control of quadrotor UAVs against actuator faults. Aerosp Sci Technol. 2023;138:108322. https:\/\/doi.org\/10.1016\/j.ast.2023.108322.","journal-title":"Aerosp Sci Technol"},{"key":"690_CR36","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-030-99584-3_26","volume-title":"Advanced Information Networking and Applications","author":"F Tlili","year":"2022","unstructured":"Tlili F, Ayed S, Chaari L, Ouni B. Artificial Intelligence Based Approach for Fault and Anomaly Detection Within UAVs. In: Barolli L, Hussain F, Enokido T, editors. Advanced Information Networking and Applications. Cham: Springer International Publishing; 2022. p. 297\u2013308."},{"key":"690_CR37","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s10846-025-02267-8","volume":"111","author":"Z Adaika","year":"2025","unstructured":"Adaika Z, Al-Haddad LA, Giernacki W, et al. Fault detection and diagnosis methodologies for unmanned aerial vehicles: state-of-the-art. J Intell Robot Syst. 2025;111:63. https:\/\/doi.org\/10.1007\/s10846-025-02267-8.","journal-title":"J Intell Robot Syst"},{"key":"690_CR38","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-05692-4","author":"LA Al-Haddad","year":"2025","unstructured":"Al-Haddad LA, Jaber AA, Hamzah MN, et al. Multiaxial vibration data for blade fault diagnosis in multirotor unmanned aerial vehicles. Sci Data. 2025. https:\/\/doi.org\/10.1038\/s41597-025-05692-4.","journal-title":"Sci Data"},{"key":"690_CR39","doi-asserted-by":"crossref","unstructured":"Puchalski R, Ko\u0142odziejczak M, Bondyra A, et al, PADRE - Propeller Anomaly Data REpository for UAVs various rotor fault configurations. In: 2023 International Conference on Unmanned Aircraft Systems (ICUAS). 2023. pp 982\u2013989","DOI":"10.1109\/ICUAS57906.2023.10156238"},{"key":"690_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/drones6110330","author":"R Puchalski","year":"2022","unstructured":"Puchalski R, Giernacki W. UAV fault detection methods, state-of-the-art. Drones. 2022. https:\/\/doi.org\/10.3390\/drones6110330.","journal-title":"Drones"},{"key":"690_CR41","doi-asserted-by":"publisher","first-page":"59","DOI":"10.3390\/robotics8030059","volume":"8","author":"G Iannace","year":"2019","unstructured":"Iannace G, Ciaburro G, Trematerra A. Fault diagnosis for UAV blades using artificial neural network. Robotics. 2019;8:59.","journal-title":"Robotics"},{"key":"690_CR42","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.ifacol.2016.07.710","volume":"49","author":"S-H Kim","year":"2016","unstructured":"Kim S-H, Negash L, Choi H-L. Cubature Kalman filter based fault detection and isolation for formation control of multi-UAVs. IFAC-PapersOnLine. 2016;49:63\u20138. https:\/\/doi.org\/10.1016\/j.ifacol.2016.07.710.","journal-title":"IFAC-PapersOnLine"},{"key":"690_CR43","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.isatra.2021.07.043","volume":"126","author":"S Liang","year":"2022","unstructured":"Liang S, Zhang S, Huang Y, et al. Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions. ISA Trans. 2022;126:472\u201385. https:\/\/doi.org\/10.1016\/j.isatra.2021.07.043.","journal-title":"ISA Trans"},{"key":"690_CR44","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.isatra.2021.07.043","volume":"126","author":"S Liang","year":"2022","unstructured":"Liang S, Zhang S, Huang Y, et al. Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions. ISA Trans. 2022;126:472\u201385.","journal-title":"ISA Trans"},{"key":"690_CR45","doi-asserted-by":"publisher","first-page":"106284","DOI":"10.1016\/j.conengprac.2025.106284","volume":"159","author":"R Li","year":"2025","unstructured":"Li R, Jiang B, Zong Y, et al. Federated fault diagnosis using data fusion in large-scale heterogeneous unmanned systems. Control Eng Pract. 2025;159:106284. https:\/\/doi.org\/10.1016\/j.conengprac.2025.106284.","journal-title":"Control Eng Pract"},{"key":"690_CR46","doi-asserted-by":"publisher","DOI":"10.1080\/15435075.2024.2448294","author":"M Irfan","year":"2024","unstructured":"Irfan M, Yasin S, Draz U, et al. Revolutionizing wind turbine fault diagnosis on supervisory control and data acquisition system with transparent artificial intelligence. Int J Green Energy. 2024. https:\/\/doi.org\/10.1080\/15435075.2024.2448294.","journal-title":"Int J Green Energy"},{"key":"690_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05584-7","author":"LA Al-Haddad","year":"2023","unstructured":"Al-Haddad LA, Jaber AA, Al-Haddad SA, Al-Muslim YM. Fault diagnosis of actuator damage in UAVs using embedded recorded data and stacked machine learning models. J Supercomput. 2023. https:\/\/doi.org\/10.1007\/s11227-023-05584-7.","journal-title":"J Supercomput"},{"key":"690_CR48","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/s40430-023-04386-5","volume":"45","author":"LA Al-Haddad","year":"2023","unstructured":"Al-Haddad LA, Jaber AA. Improved UAV blade unbalance prediction based on machine learning and ReliefF supreme feature ranking method. J Braz Soc Mech Sci Eng. 2023;45:463. https:\/\/doi.org\/10.1007\/s40430-023-04386-5.","journal-title":"J Braz Soc Mech Sci Eng"},{"key":"690_CR49","doi-asserted-by":"publisher","first-page":"18599","DOI":"10.1038\/s41598-024-69462-9","volume":"14","author":"LA Al-Haddad","year":"2024","unstructured":"Al-Haddad LA, Giernacki W, Basem A, et al. UAV propeller fault diagnosis using deep learning of non-traditional \u03c72-selected Taguchi method-tested Lempel-Ziv complexity and Teager-Kaiser energy features. Sci Rep. 2024;14:18599. https:\/\/doi.org\/10.1038\/s41598-024-69462-9.","journal-title":"Sci Rep"},{"key":"690_CR50","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3390\/en12010095","volume":"12","author":"NP Nguyen","year":"2018","unstructured":"Nguyen NP, Hong SK. Fault-tolerant control of quadcopter UAVs using robust adaptive sliding mode approach. Energies (Basel). 2018;12:95.","journal-title":"Energies (Basel)"},{"key":"690_CR51","doi-asserted-by":"publisher","first-page":"5263","DOI":"10.1016\/j.ifacol.2017.08.468","volume":"50","author":"M Saied","year":"2017","unstructured":"Saied M, Lussier B, Fantoni I, et al. Fault diagnosis and fault-tolerant control of an octorotor UAV using motors speeds measurements. IFAC-PapersOnLine. 2017;50:5263\u20138. https:\/\/doi.org\/10.1016\/j.ifacol.2017.08.468.","journal-title":"IFAC-PapersOnLine"},{"key":"690_CR52","doi-asserted-by":"publisher","first-page":"518","DOI":"10.3390\/aerospace9090518","volume":"9","author":"S Ai","year":"2022","unstructured":"Ai S, Song J, Cai G, Zhao K. Active fault-tolerant control for quadrotor UAV against sensor fault diagnosed by the auto sequential random forest. Aerospace. 2022;9:518.","journal-title":"Aerospace"},{"key":"690_CR53","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace9090518","author":"S Ai","year":"2022","unstructured":"Ai S, Song J, Cai G, Zhao K. Active fault-tolerant control for quadrotor UAV against sensor fault diagnosed by the auto sequential random forest. Aerospace. 2022. https:\/\/doi.org\/10.3390\/aerospace9090518.","journal-title":"Aerospace"},{"key":"690_CR54","doi-asserted-by":"publisher","DOI":"10.3390\/machines9120360","author":"P Yang","year":"2021","unstructured":"Yang P, Wen C, Geng H, Liu P. Intelligent fault diagnosis method for blade damage of quad-rotor UAV based on stacked pruning sparse denoising autoencoder and convolutional neural network. Machines. 2021. https:\/\/doi.org\/10.3390\/machines9120360.","journal-title":"Machines"},{"key":"690_CR55","doi-asserted-by":"publisher","DOI":"10.3390\/s22197355","author":"J Song","year":"2022","unstructured":"Song J, Shang W, Ai S, Zhao K. Model and data-driven combination: a fault diagnosis and localization method for unknown fault size of quadrotor UAV actuator based on extended state observer and deep forest. Sensors. 2022. https:\/\/doi.org\/10.3390\/s22197355.","journal-title":"Sensors"},{"key":"690_CR56","doi-asserted-by":"publisher","first-page":"23239","DOI":"10.1109\/JSEN.2024.3405630","volume":"24","author":"C Li","year":"2024","unstructured":"Li C, Luo K, Yang L, et al. A zero-shot fault detection method for UAV sensors based on a novel CVAE-GAN model. IEEE Sens J. 2024;24:23239\u201354. https:\/\/doi.org\/10.1109\/JSEN.2024.3405630.","journal-title":"IEEE Sens J"},{"key":"690_CR57","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.isatra.2023.02.026","volume":"138","author":"T Thanaraj","year":"2023","unstructured":"Thanaraj T, Low KH, Ng BF. Actuator fault detection and isolation on multi-rotor UAV using extreme learning neuro-fuzzy systems. ISA Trans. 2023;138:168\u201385. https:\/\/doi.org\/10.1016\/j.isatra.2023.02.026.","journal-title":"ISA Trans"},{"key":"690_CR58","doi-asserted-by":"publisher","first-page":"5410","DOI":"10.3390\/app11125410","volume":"11","author":"K Zheng","year":"2021","unstructured":"Zheng K, Jia G, Yang L, Wang J. A compound fault labeling and diagnosis method based on flight data and BIT record of UAV. Appl Sci. 2021;11:5410.","journal-title":"Appl Sci"},{"key":"690_CR59","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1016\/j.ifacol.2019.12.066","volume":"52","author":"M Saied","year":"2019","unstructured":"Saied M, Mahairy T, Francis C, et al. Differential flatness-based approach for sensors and actuators fault diagnosis of a multirotor UAV. IFAC-PapersOnLine. 2019;52:831\u20136. https:\/\/doi.org\/10.1016\/j.ifacol.2019.12.066.","journal-title":"IFAC-PapersOnLine"},{"key":"690_CR60","doi-asserted-by":"publisher","first-page":"104683","DOI":"10.1016\/j.micpro.2022.104683","volume":"94","author":"O Yaman","year":"2022","unstructured":"Yaman O, Yol F, Altinors A. A fault detection method based on embedded feature extraction and SVM classification for UAV motors. Microprocess Microsyst. 2022;94:104683. https:\/\/doi.org\/10.1016\/j.micpro.2022.104683.","journal-title":"Microprocess Microsyst"},{"key":"690_CR61","doi-asserted-by":"crossref","unstructured":"Ko\u0142odziejczak M, Puchalski R, Bondyra A, et al, Toward lightweight acoustic fault detection and identification of UAV rotors. In: 2023 International Conference on Unmanned Aircraft Systems (ICUAS). 2023; pp 990\u2013997","DOI":"10.1109\/ICUAS57906.2023.10156624"},{"key":"690_CR62","doi-asserted-by":"publisher","first-page":"3955","DOI":"10.3390\/en15113955","volume":"15","author":"A Bondyra","year":"2022","unstructured":"Bondyra A, Ko\u0142odziejczak M, Kulikowski R, Giernacki W. An acoustic fault detection and isolation system for multirotor UAV. Energies. 2022;15:3955.","journal-title":"Energies"},{"key":"690_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.xpro.2024.103351","author":"LA Al-Haddad","year":"2024","unstructured":"Al-Haddad LA, Jaber AA, Mahdi NM, et al. Protocol for UAV fault diagnosis using signal processing and machine learning. STAR Protoc. 2024. https:\/\/doi.org\/10.1016\/j.xpro.2024.103351.","journal-title":"STAR Protoc"},{"key":"690_CR64","doi-asserted-by":"publisher","first-page":"12095","DOI":"10.1007\/s10489-024-05733-2","volume":"54","author":"E \u00c7etin","year":"2024","unstructured":"\u00c7etin E, Barrado C, Salam\u00ed E, Pastor E. Analyzing deep reinforcement learning model decisions with Shapley additive explanations for counter drone operations. Appl Intell. 2024;54:12095\u2013111. https:\/\/doi.org\/10.1007\/s10489-024-05733-2.","journal-title":"Appl Intell"},{"key":"690_CR65","doi-asserted-by":"publisher","DOI":"10.3390\/app14135487","author":"Y-W Hong","year":"2024","unstructured":"Hong Y-W, Yoo D-Y. multiple intrusion detection using Shapley additive explanations and a heterogeneous ensemble model in an unmanned aerial vehicle\u2019s controller area network. Appl Sci. 2024. https:\/\/doi.org\/10.3390\/app14135487.","journal-title":"Appl Sci"},{"key":"690_CR66","doi-asserted-by":"crossref","unstructured":"Ajakwe SO, Kim D-S Time Sensitive Anti-Infoswarm Agnostic Intelligence for Safe UAV Communication. In: 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). 2024; pp 1614\u20131619","DOI":"10.1109\/ICTC62082.2024.10827074"},{"key":"690_CR67","doi-asserted-by":"crossref","unstructured":"Haque E, Hasan K, Ahmed I, et al (2024) Towards an interpretable AI framework for advanced classification of unmanned aerial vehicles (UAVs). In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC). pp 644\u2013645","DOI":"10.1109\/CCNC51664.2024.10454862"},{"key":"690_CR68","doi-asserted-by":"crossref","unstructured":"Jalayer M, Shojaeinasab A, Najjaran H, A model identification forensics approach for signal-based condition monitoring. In: Silva FJG, Ferreira LP, S\u00e1 JC, et al (eds) Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems. Springer Nature Switzerland, Cham, 2024. pp 12\u201319","DOI":"10.1007\/978-3-031-38165-2_2"},{"key":"690_CR69","doi-asserted-by":"publisher","first-page":"6636794","DOI":"10.1155\/2021\/6636794","volume":"2021","author":"X Ma","year":"2021","unstructured":"Ma X, Zhang X, Wang H, et al. An operational safety evaluation method for manned transport aircraft and large UAV in mixed airspace. Math Probl Eng. 2021;2021:6636794. https:\/\/doi.org\/10.1155\/2021\/6636794.","journal-title":"Math Probl Eng"},{"key":"690_CR70","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.tranpol.2022.06.006","volume":"125","author":"IL Henderson","year":"2022","unstructured":"Henderson IL. Aviation safety regulations for unmanned aircraft operations: perspectives from users. Transp Policy. 2022;125:192\u2013206. https:\/\/doi.org\/10.1016\/j.tranpol.2022.06.006.","journal-title":"Transp Policy"},{"key":"690_CR71","doi-asserted-by":"publisher","DOI":"10.3390\/rs9050459","author":"C St\u00f6cker","year":"2017","unstructured":"St\u00f6cker C, Bennett R, Nex F, et al. Review of the current state of UAV regulations. Remote Sens. 2017. https:\/\/doi.org\/10.3390\/rs9050459.","journal-title":"Remote Sens"},{"key":"690_CR72","doi-asserted-by":"publisher","first-page":"102218","DOI":"10.1016\/j.jairtraman.2022.102218","volume":"102","author":"M Grote","year":"2022","unstructured":"Grote M, Pilko A, Scanlan J, et al. Sharing airspace with uncrewed aerial vehicles (UAVs): views of the general aviation (GA) community. J Air Transp Manag. 2022;102:102218. https:\/\/doi.org\/10.1016\/j.jairtraman.2022.102218.","journal-title":"J Air Transp Manag"},{"key":"690_CR73","doi-asserted-by":"crossref","unstructured":"Bassi E, European drones regulation: today\u2019s legal challenges. In: 2019 International Conference on Unmanned Aircraft Systems (ICUAS). 2019. pp 443\u2013450","DOI":"10.1109\/ICUAS.2019.8798173"},{"key":"690_CR74","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1007\/s11023-019-09511-9","volume":"29","author":"E Bassi","year":"2019","unstructured":"Bassi E, Bloise N, Dirutigliano J, et al. The design of GDPR-abiding drones through flight operation maps: a win\u2013win approach to data protection, aerospace engineering, and risk management. Minds Mach. 2019;29:579\u2013601. https:\/\/doi.org\/10.1007\/s11023-019-09511-9.","journal-title":"Minds Mach"},{"key":"690_CR75","doi-asserted-by":"crossref","unstructured":"Thompson AK, Patel DD, Akba\u015f M\u0130, Formulating a Comprehensive Cybersecurity Framework for Uncrewed Aerial Vehicles. In: 2024 IEEE International Performance, Computing, and Communications Conference (IPCCC). 2024. pp 1\u20136","DOI":"10.1109\/IPCCC59868.2024.10850308"},{"key":"690_CR76","doi-asserted-by":"crossref","unstructured":"Geister R, Peinecke N, Sundqvist B-G, et al, On-Board System Concept for Drones in the European U-space. In: 2019 IEEE\/AIAA 38th Digital Avionics Systems Conference (DASC). 2019. pp 1\u20136","DOI":"10.1109\/DASC43569.2019.9081661"},{"key":"690_CR77","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s10846-022-01714-0","volume":"106","author":"C Janke","year":"2022","unstructured":"Janke C, de Haag MU. Implementation of European drone regulations\u2014status quo and assessment. J Intell Robot Syst. 2022;106:33. https:\/\/doi.org\/10.1007\/s10846-022-01714-0.","journal-title":"J Intell Robot Syst"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00690-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00690-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00690-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T21:25:18Z","timestamp":1767043518000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00690-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,23]]},"references-count":77,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["690"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00690-2","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,23]]},"assertion":[{"value":"5 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"410"}}