Advances in Data-Driven Engineering for Aerospace Non-destructive Evaluation and Structural Health Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 20 July 2024 | Viewed by 229
Special Issue Editors
Interests: fault diagnosis; AI for nondestructive testing and structural health monitoring
Interests: machine learning; artificial intelligence; computational intelligence; data analytics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the transformative impact of data-driven engineering and machine learning in aerospace, particularly in non-destructive evaluation and structural health monitoring of complex aerospace compoents. This interdisciplinary approach aims to improve aerospace industry practices by leveraging big data, advanced computation, and ML algorithms to solve complex, multi-objective optimization problems in aircraft manufacturing and matainence. The scope focuses on the need for interpretable, generalizable, and certifiable ML techniques for safety-critical applications in aerospace. Key themes include the utility of ML in enhancing decision-making processes, the role of high-fidelity simulations, and the importance of data quality and management. This Special Issue examines how ML algorithms, coupled with advanced sensor technologies, are revolutionizing non-destructive evaluation and structural health monitoring, leading to unprecedented levels of safety and efficiency. It underscores the transformative potential of ML in reshaping aerospace engineering, making it a critical area of study and innovation within the broader context of sensor technologies and their applications.
Dr. Qiuji Yi
Prof. Dr. Wai Lok Woo
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning and AI
- aerospace engineering
- non-destructive evaluation
- structural health monitoring
- interpretable ML models
- spatiotemporal analysis
- sparsity-promoting techniques
- physics-informed ML
