LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites
Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/7/2925 |
| _version_ | 1797212818897895424 |
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| author | Syed Haider Mehdi Rizvi Muntazir Abbas Syed Sajjad Haider Zaidi Muhammad Tayyab Adil Malik |
| author_facet | Syed Haider Mehdi Rizvi Muntazir Abbas Syed Sajjad Haider Zaidi Muhammad Tayyab Adil Malik |
| author_sort | Syed Haider Mehdi Rizvi |
| collection | DOAJ |
| description | Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency. |
| first_indexed | 2024-04-24T10:48:26Z |
| format | Article |
| id | doaj.art-087ac8401d154a6387b254a94b1e2bc0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:48:26Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-087ac8401d154a6387b254a94b1e2bc02024-04-12T13:15:12ZengMDPI AGApplied Sciences2076-34172024-03-01147292510.3390/app14072925LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in CompositesSyed Haider Mehdi Rizvi0Muntazir Abbas1Syed Sajjad Haider Zaidi2Muhammad Tayyab3Adil Malik4Department of Engineering Sciences, PN Engineering College, National University of Science and Technology, Karachi 75350, PakistanDepartment of Engineering Sciences, PN Engineering College, National University of Science and Technology, Karachi 75350, PakistanDepartment of Engineering Sciences, PN Engineering College, National University of Science and Technology, Karachi 75350, PakistanDepartment of Engineering Sciences, PN Engineering College, National University of Science and Technology, Karachi 75350, PakistanDepartment of Engineering Sciences, PN Engineering College, National University of Science and Technology, Karachi 75350, PakistanLamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency.https://www.mdpi.com/2076-3417/14/7/2925structural health monitoringdeep learningLamb wavesautoencoderanomaly detection |
| spellingShingle | Syed Haider Mehdi Rizvi Muntazir Abbas Syed Sajjad Haider Zaidi Muhammad Tayyab Adil Malik LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites Applied Sciences structural health monitoring deep learning Lamb waves autoencoder anomaly detection |
| title | LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites |
| title_full | LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites |
| title_fullStr | LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites |
| title_full_unstemmed | LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites |
| title_short | LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites |
| title_sort | lstm based autoencoder with maximal overlap discrete wavelet transforms using lamb wave for anomaly detection in composites |
| topic | structural health monitoring deep learning Lamb waves autoencoder anomaly detection |
| url | https://www.mdpi.com/2076-3417/14/7/2925 |
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