Rizvi, S.H.M.; Abbas, M.; Zaidi, S.S.H.; Tayyab, M.; Malik, A. LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites. Appl. Sci.2024, 14, 2925.
Rizvi, S.H.M.; Abbas, M.; Zaidi, S.S.H.; Tayyab, M.; Malik, A. LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites. Appl. Sci. 2024, 14, 2925.
Rizvi, S.H.M.; Abbas, M.; Zaidi, S.S.H.; Tayyab, M.; Malik, A. LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites. Appl. Sci.2024, 14, 2925.
Rizvi, S.H.M.; Abbas, M.; Zaidi, S.S.H.; Tayyab, M.; Malik, A. LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites. Appl. Sci. 2024, 14, 2925.
Abstract
Lamb wave-based structural health monitoring is widely acknowledged as a reliable 11 method for damage identification, classification, localization and quantification. However, due to 12 the complexity of Lamb wave signals, especially after interacting with structural components and 13 defects, interpreting these waves and extracting useful information about the structure's health is 14 still a major challenge. However, deep learning-based strategy offers a great opportunity to address 15 such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike 16 traditional methods, which often require careful feature engineering and preprocessing, deep learn-17 ing can automatically extract relevant features from the raw data. This paper proposes an autoen-18 coder based on a bidirectional long short-term memory network with maximal overlap discrete 19 wavelet transform layer to detect the signal anomaly and determine the location of the damage in 20 the composite structure. This approach has the potential to greatly enhance our ability to detect and 21 locate structural damage in composite structures, thereby increasing safety and efficiency.
Keywords
Structural Health Monitoring; Deep Learning; Lamb waves; Autoencoder; Anomaly Detection
Subject
Engineering, Safety, Risk, Reliability and Quality
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
