PSI - Issue 80

ScienceDirect Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2025) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2025) 000 – 000 Available online at www.sciencedirect.com Procedia Structural Integrity 80 (2026) 53–64

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© 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Ferri Aliabadi Abstract This paper presents a simple K-SVD dictionary learning method for transferable, baseline-free damage identification on composite coupon panels, relying solely on the analytical excitation. The analytical signal, encapsulating universal patterns of Lamb wave pristine signals, can be segmented, and reorganized as ‘shapelets’. These shapelets serve as a universal function to reconstr uct baseline signals and further facilitate damage identification. The key innovation lies in the pure use of the analytical 5-cycle Hanning-windowed toneburst signal instead of requiring extensive experimental data, which minimizes data collection efforts and significantly lowers computational time. The proposed methodology is comprised of four steps: candidate generation, K-SVD dictionary learning, dictionary compilation and sparse coding, which has a wide application to different materials, temperature fields, specimen sizes, sensor configurations, etc. Prior to the implementation of the proposed method, a standard signal pre processing pipeline is operated to enhance the data quality. In this study, experimental comparisons were conducted between the proposed baseline-free K-SVD, time reversal (TR), instantaneous baseline (IB), and reciprocity approach, leveraging data from composite coupon panels. Furthermore, it was extended to detect the barely visible impact damage (BVID) under a temperature range of [20: 5: 50] ℃ . Consequently, the effectiveness and robustness of the method have been validated based on the high accuracy and consistent localization performance across different damage scenarios. Keywords: Analytical signal; baseline-free; K-SVD dictionary learning; sparse coding; damage identification 1. Introduction Composite materials are increasingly in demand within the aerospace and aviation industry due to their high strength to-weight ratios, corrosion resistance, and long-term durability. These materials are widely used in manufacturing of core aircraft components, such as wind blades and fuselage panels [1, 2]. However, airframes are often exposed to unpredictable and fluctuating loading conditions during operation, which can induce barely visible impact damages (BVIDs) across multiple structural components [3, 4] . These BVIDs can compromise the airframe’s mechanical Fracture, Damage and Structural Health Monitoring A simple K-SVD dictionary learning method for baseline-free damage identification purely based on analytical excitation. Haomiao Fang a* , Zahra Sharif Khodaei a and Ferri M. H. Aliabadi a* a Department of Aeronautics, Imperial College London, South Kensington Campus, Exhibition Road, SW7 2AZ, London, UK Abstract This paper presents a simple K-SVD dictionary learning method for transferable, baseline-free damage identification on composite coupon panels, relying solely on the analytical excitation. The analytical signal, encapsulating universal patterns of Lamb wave pristine signals, can be segmented, and reorganized as ‘shapelets’. These shapelets serve as a universal function to reconstr uct baseline signals and further facilitate damage identification. The key innovation lies in the pure use of the analytical 5-cycle Hanning-windowed toneburst signal instead of requiring extensive experimental data, which minimizes data collection efforts and significantly lowers computational time. The proposed methodology is comprised of four steps: candidate generation, K-SVD dictionary learning, dictionary compilation and sparse coding, which has a wide application to different materials, temperature fields, specimen sizes, sensor configurations, etc. Prior to the implementation of the proposed method, a standard signal pre processing pipeline is operated to enhance the data quality. In this study, experimental comparisons were conducted between the proposed baseline-free K-SVD, time reversal (TR), instantaneous baseline (IB), and reciprocity approach, leveraging data from composite coupon panels. Furthermore, it was extended to detect the barely visible impact damage (BVID) under a temperature range of [20: 5: 50] ℃ . Consequently, the effectiveness and robustness of the method have been validated based on the high accuracy and consistent localization performance across different damage scenarios. Keywords: Analytical signal; baseline-free; K-SVD dictionary learning; sparse coding; damage identification 1. Introduction Composite materials are increasingly in demand within the aerospace and aviation industry due to their high strength to-weight ratios, corrosion resistance, and long-term durability. These materials are widely used in manufacturing of core aircraft components, such as wind blades and fuselage panels [1, 2]. However, airframes are often exposed to unpredictable and fluctuating loading conditions during operation, which can induce barely visible impact damages (BVIDs) across multiple structural components [3, 4] . These BVIDs can compromise the airframe’s mechanical 2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi 2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi Fracture, Damage and Structural Health Monitoring A simple K-SVD dictionary learning method for baseline-free damage identification purely based on analytical excitation. Haomiao Fang a* , Zahra Sharif Khodaei a and Ferri M. H. Aliabadi a* a Department of Aeronautics, Imperial College London, South Kensington Campus, Exhibition Road, SW7 2AZ, London, UK * Corresponding author. E-mail address : haomiao.fang22@imperial.ac.uk * Corresponding author. E-mail address : haomiao.fang22@imperial.ac.uk

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Ferri Aliabadi 10.1016/j.prostr.2026.02.006