This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Dr. Kazi Kutubuddin Sayyad Liyakat,
- Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur (MS), Maharashtra, India
Abstract
The synergistic combination of modern sensors including artificial intelligence (AI) has significantly changed the aeronautics industry’s ongoing quest for increased safety, efficiency, and autonomy. The examination of the critical role these technologies play throughout the whole aerospace lifecycle from design and production to flight operations and maintenance is examined in this research. The eyes and ears of contemporary aircraft, sensors give an unparalleled amount and quality of real-time data about ambient conditions, structural integrity, system function, and pilot intent. The foundation for intelligent decision-making is this data, which ranges from conventional metrics like altitude and airspeed to intricate signatures from lidar, hyperspectral photography, and monitoring of structural health systems. By using complex algorithms for recognition of patterns, anomaly detection, predictive analytics, and control system optimization, AI then unleashes the potential of this deluge of data. This paper explores particular applications, such as intelligent defect diagnostics and prognostics, flight autonomy systems, enhanced situational awareness for pilots, AI-powered navigation and guiding, and air traffic management optimization. The next wave of aeronautical innovations is being made possible by the transformative effect of integrating sensor technology with AI, which is clearly producing more durable, flexible, and eventually safer aerial vehicles in modern aviation systems.
Keywords: Aerospace, Sensors, AI, Manufacturing, Maintenance, Operation,
Dr. Kazi Kutubuddin Sayyad Liyakat. A Study on the Use of AI and Sensors in Aerospace. Journal of Aerospace Engineering & Technology. 2026; 16(01):-.
Dr. Kazi Kutubuddin Sayyad Liyakat. A Study on the Use of AI and Sensors in Aerospace. Journal of Aerospace Engineering & Technology. 2026; 16(01):-. Available from: https://journals.stmjournals.com/joaet/article=2026/view=243307
References
[1]. Ribeiro LC, Korsa MT, Saotome O, Adam J, Hansen RM. Design of a micro-machined flow sensor for aircraft air data systems application: Mechanical considerations. InProceedings of the International Conference on Microelectronics and Microsystems Technology ICMMT 2020 Dec (pp. 10-11)
[2]. Pagonis DN, Benaki V, Kaltsas G, Pagonis A. Design of a mass air flow sensor employing additive manufacturing and standard airfoil geometry. Applied Sciences. 2021 Dec 7;11(24):11579.
[3]. Kumar A, Wang S, Shaikh AM, Bilal H, Lu B, Song S. Building on prior lightweight CNN model combined with LSTM-AM framework to guide fault detection in fixed-wing UAVs. International Journal of Machine Learning and Cybernetics. 2024 Sep;15(9):4175-91.
[4]. Kazi KS. Machine learning (ML)-based Braille Lippi characters and numbers detection and announcement system for blind children in learning. InSocial Reflections of Human- Computer Interaction in Education, Management, and Economics 2024 (pp. 16-39). IGI Global Scientific Publishing.
[5]. Kazi KS. ChatGPT: An automated teacher’s guide to learning. InAI Algorithms and ChatGPT for Student Engagement in Online Learning 2024 (pp. 1-20). IGI Global Scientific Publishing.
[6]. Liyakat KK. Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. InInternational Conference on Machine Learning, IoT and Big Data 2023 Mar 10 (pp. 123-134). Singapore: Springer Nature Singapore.
[7]. Rakesh Kumar C, Naveen R, Kowsalya, Sakri FM, Prasath MS. Utilizing AI Techniques for Detecting Damage in Aerospace Applications. Artificial Intelligence Applications in Aeronautical and Aerospace Engineering. 2025 Oct 15:133-50.
[8]. Shenwai PG, Choudhary A, Pokuri T, Basak A, Manikandan M, Singh B. On the Role of Artificial Intelligence in Aerospace Engineering: Current State of the Art and Future Trajectories. The Aeronautical Journal. 2025 Aug 11:1-27.
[9]. Oladosu MA, Abah MA, Nasiru M, Oladosu OA. Application of Artificial Intelligence in Aerospace Structural Health Monitoring: A Systematic Review and Future Perspectives. J. Eng. Ind. Res. 2026;7(3).
[10]. Das M, Sahu S, Parhi DR. Composite materials and their damage detection using AI techniques for aerospace application: A brief review. Materials Today: Proceedings. 2021 Jan 1;44:955-60.
Journal of Aerospace Engineering & Technology
| Volume | 16 |
| 01 | |
| Received | 17/03/2026 |
| Accepted | 19/03/2026 |
| Published | 09/05/2026 |
| Publication Time | 53 Days |
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