Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture Model
Composite laminates are widely used in aerospace, automotive, construction, and luxury industries, owing to their superior mechanical properties and design flexibility. However, detecting manufacturing defects and in-service damage remains a vital challenge for structural safety. While traditional u...
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MDPI AG
2025-07-01
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| author | Olivier Munyaneza Jung Woo Sohn |
| author_facet | Olivier Munyaneza Jung Woo Sohn |
| author_sort | Olivier Munyaneza |
| collection | DOAJ |
| description | Composite laminates are widely used in aerospace, automotive, construction, and luxury industries, owing to their superior mechanical properties and design flexibility. However, detecting manufacturing defects and in-service damage remains a vital challenge for structural safety. While traditional unsupervised machine learning methods have been used in structural health monitoring (SHM), their high false positive rates limit their reliability in real-world applications. This issue is mostly inherited from their limited ability to capture small temporal variations in Lamb wave signals and their dependence on shallow architectures that suffer with complex signal distributions, causing the misclassification of damaged signals as healthy data. To address this, we suggested an unsupervised anomaly detection framework that integrates a self-attention autoencoder with a Gaussian mixture model (SAE-GMM). The model is solely trained on healthy Lamb wave signals, including high-quality synthetic data generated via a generative adversarial network (GAN). Damages are detected through reconstruction errors and probabilistic clustering in the latent space. The self-attention mechanism enhances feature representation by capturing subtle temporal dependencies, while the GMM enables a solid separation among signals. Experimental results demonstrated that the proposed model (SAE-GMM) achieves high detection accuracy, a low false positive rate, and strong generalization under varying noise conditions, outperforming traditional and deep learning baselines. |
| format | Article |
| id | doaj-art-69426417c24d4ce98ab0bb68b5719e7f |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-69426417c24d4ce98ab0bb68b5719e7f2025-08-20T03:36:34ZengMDPI AGMathematics2227-73902025-07-011315244510.3390/math13152445Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture ModelOlivier Munyaneza0Jung Woo Sohn1Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Graduated School, Kumoh National Institute of Technology, Daehak-ro 61, Gumi 39177, Republic of KoreaSchool of Mechanical Engineering, Kumoh National Institute of Technology, Daehak-ro 61, Gumi 39177, Republic of KoreaComposite laminates are widely used in aerospace, automotive, construction, and luxury industries, owing to their superior mechanical properties and design flexibility. However, detecting manufacturing defects and in-service damage remains a vital challenge for structural safety. While traditional unsupervised machine learning methods have been used in structural health monitoring (SHM), their high false positive rates limit their reliability in real-world applications. This issue is mostly inherited from their limited ability to capture small temporal variations in Lamb wave signals and their dependence on shallow architectures that suffer with complex signal distributions, causing the misclassification of damaged signals as healthy data. To address this, we suggested an unsupervised anomaly detection framework that integrates a self-attention autoencoder with a Gaussian mixture model (SAE-GMM). The model is solely trained on healthy Lamb wave signals, including high-quality synthetic data generated via a generative adversarial network (GAN). Damages are detected through reconstruction errors and probabilistic clustering in the latent space. The self-attention mechanism enhances feature representation by capturing subtle temporal dependencies, while the GMM enables a solid separation among signals. Experimental results demonstrated that the proposed model (SAE-GMM) achieves high detection accuracy, a low false positive rate, and strong generalization under varying noise conditions, outperforming traditional and deep learning baselines.https://www.mdpi.com/2227-7390/13/15/2445composite laminatesunsupervised machine learningstructural healthy monitoringself-attentionGaussian mixture modelgenerative adversarial network |
| spellingShingle | Olivier Munyaneza Jung Woo Sohn Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture Model Mathematics composite laminates unsupervised machine learning structural healthy monitoring self-attention Gaussian mixture model generative adversarial network |
| title | Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture Model |
| title_full | Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture Model |
| title_fullStr | Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture Model |
| title_full_unstemmed | Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture Model |
| title_short | Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture Model |
| title_sort | anomaly detection on laminated composite plate using self attention autoencoder and gaussian mixture model |
| topic | composite laminates unsupervised machine learning structural healthy monitoring self-attention Gaussian mixture model generative adversarial network |
| url | https://www.mdpi.com/2227-7390/13/15/2445 |
| work_keys_str_mv | AT oliviermunyaneza anomalydetectiononlaminatedcompositeplateusingselfattentionautoencoderandgaussianmixturemodel AT jungwoosohn anomalydetectiononlaminatedcompositeplateusingselfattentionautoencoderandgaussianmixturemodel |