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- Vol. 7 Issue 3
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Advance Safety Solutions for Boeing Aircraft’s Using Artificial Intelligence and WSN
Authors
1,2.3B.E. Students, Department of Electronics and Communication Engineering, BGS Institute of Technology, Adichunchanagiri University, B. G. Nagara, India. 4Assistant Professor, Department of Electronics and Communication Engineering, BGS Institute of Technology, Adichunchanagiri University, B. G. Nagara, India. 5Associate Professor, Department of Electronics and Communication Engineering, BGS Institute of Technology, Adichunchanagiri University, B. G. Nagara, India.
Article Information
- Published In
- Volume 7, Issue 3 (2026)
- Published Online
- May-June 2026
- Pages
- 261-266
- Article Type
- Original Article
- Check for updates
-
References
1. I. Kabashkin and V. Perekrestov, “AI-Driven Fault Detection and Maintenance Optimization via Aviation Technical Support as a
Service (ATSaaS),” 2025.
2. Uzun et al., “Damage Detection in Aircraft Engine Borescope Inspection Using Deep Learning,” 2025.
3. X. Chen et al., “A Multi-Rate Sensor Fusion and Multi-Task Learning Network for Concurrent Fault Diagnosis,” 2025.
4. W. Fan et al., “Data Fusion for Optimal Condition-Based Aircraft Fleet Maintenance,” 2024.
5. A. Plastropoulos et al., “Aircraft Skin Machine Learning-Based Defect Detection and Size Estimation in Visual Inspection,” 2024.
6. T. Nguyen et al., “Real-Time Aircraft Health Monitoring Using Edge–Cloud Hybrid Analytics,” 2024.
7. S. Lee et al., “Deep Reinforcement Learning for Predictive Aircraft Maintenance and Scheduling,” 2023.
8. A. Upadhyay et al., “A Deep-Learning-Based Approach for Aircraft Engine Defect Detection for Borescope Inspection,” 2023.
9. G. M. Bisanti et al., “Digital Twins for Aircraft Maintenance and Operation,” 2023.
10. R. Patel et al., “Predictive Maintenance of Aircraft Components Based on Machine Learning Classification Algorithms,” 2023.
11. M. D. Dangut et al., “Prediction of Failures in Aircraft Parts Using Hybrid Deep Learning Approach,” 2023.
12. P. Sharma et al., “Aircraft Fault Detection Using Artificial Intelligence: A Review,” 2023.
13. S. Kar, T. Dey, and R. Mukherjee, “IoT Predictive Maintenance for Aircraft,” 2023.
14. Y. Zhang et al., “Hybrid Physics-Informed Neural Networks for Aircraft Structural Fatigue Prediction,” 2023.
Service (ATSaaS),” 2025.
2. Uzun et al., “Damage Detection in Aircraft Engine Borescope Inspection Using Deep Learning,” 2025.
3. X. Chen et al., “A Multi-Rate Sensor Fusion and Multi-Task Learning Network for Concurrent Fault Diagnosis,” 2025.
4. W. Fan et al., “Data Fusion for Optimal Condition-Based Aircraft Fleet Maintenance,” 2024.
5. A. Plastropoulos et al., “Aircraft Skin Machine Learning-Based Defect Detection and Size Estimation in Visual Inspection,” 2024.
6. T. Nguyen et al., “Real-Time Aircraft Health Monitoring Using Edge–Cloud Hybrid Analytics,” 2024.
7. S. Lee et al., “Deep Reinforcement Learning for Predictive Aircraft Maintenance and Scheduling,” 2023.
8. A. Upadhyay et al., “A Deep-Learning-Based Approach for Aircraft Engine Defect Detection for Borescope Inspection,” 2023.
9. G. M. Bisanti et al., “Digital Twins for Aircraft Maintenance and Operation,” 2023.
10. R. Patel et al., “Predictive Maintenance of Aircraft Components Based on Machine Learning Classification Algorithms,” 2023.
11. M. D. Dangut et al., “Prediction of Failures in Aircraft Parts Using Hybrid Deep Learning Approach,” 2023.
12. P. Sharma et al., “Aircraft Fault Detection Using Artificial Intelligence: A Review,” 2023.
13. S. Kar, T. Dey, and R. Mukherjee, “IoT Predictive Maintenance for Aircraft,” 2023.
14. Y. Zhang et al., “Hybrid Physics-Informed Neural Networks for Aircraft Structural Fatigue Prediction,” 2023.
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