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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.01081 |
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| _version_ | 1866909718524461056 |
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| author | Liao, Yunlai Wang, Yihan Fang, Chen Yang, Xin Zeng, Xianping Chronopoulos, Dimitrios Qing, Xinlin |
| author_facet | Liao, Yunlai Wang, Yihan Fang, Chen Yang, Xin Zeng, Xianping Chronopoulos, Dimitrios Qing, Xinlin |
| contents | Structural health monitoring (SHM) ensures the safety and longevity of structures such as aerospace equipment and wind power installations. Developing a simple, highly flexible, and scalable SHM method that does not depend on baseline models is significant for ensuring the operational integrity of advanced composite structures. In this regard, a hybrid baseline-free damage detection and localization framework incorporating an unsupervised Kolmogorov-Arnold autoencoder (KAE) and modified probabilistic elliptical imaging algorithm (MRAPID) is proposed for damage detection and localization in composite structures. Specifically, KAE was used to process the guided wave signals (GW) without any prior feature extraction process. The KAE continuously learns and adapts to the baseline model of each structure, learning from the response characteristics of its undamaged state. Then, the predictions from KAE are processed, combined with the MRAPID to generate a damage probability map. The performance of the proposed method for damage detection and localization was verified using the simulated damage data obtained on wind turbine blades and the actual damage data obtained on composite flat plates. The results show that the proposed method can effectively detect and localize damage and can achieve multiple damage localization. In addition, the method outperforms classical damage detection algorithms and state-of-the-art baseline-free damage detection and localization methods in terms of damage localization accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Baseline-free Damage Detection and Localization on Composite Structures with Unsupervised Kolmogorov-Arnold Autoencoder and Guided Waves Liao, Yunlai Wang, Yihan Fang, Chen Yang, Xin Zeng, Xianping Chronopoulos, Dimitrios Qing, Xinlin Computational Engineering, Finance, and Science Structural health monitoring (SHM) ensures the safety and longevity of structures such as aerospace equipment and wind power installations. Developing a simple, highly flexible, and scalable SHM method that does not depend on baseline models is significant for ensuring the operational integrity of advanced composite structures. In this regard, a hybrid baseline-free damage detection and localization framework incorporating an unsupervised Kolmogorov-Arnold autoencoder (KAE) and modified probabilistic elliptical imaging algorithm (MRAPID) is proposed for damage detection and localization in composite structures. Specifically, KAE was used to process the guided wave signals (GW) without any prior feature extraction process. The KAE continuously learns and adapts to the baseline model of each structure, learning from the response characteristics of its undamaged state. Then, the predictions from KAE are processed, combined with the MRAPID to generate a damage probability map. The performance of the proposed method for damage detection and localization was verified using the simulated damage data obtained on wind turbine blades and the actual damage data obtained on composite flat plates. The results show that the proposed method can effectively detect and localize damage and can achieve multiple damage localization. In addition, the method outperforms classical damage detection algorithms and state-of-the-art baseline-free damage detection and localization methods in terms of damage localization accuracy. |
| title | Baseline-free Damage Detection and Localization on Composite Structures with Unsupervised Kolmogorov-Arnold Autoencoder and Guided Waves |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2508.01081 |