###### Abstract  
As laminated composites are applied more commonly, Prognostics and Health Management (PHM) techniques for the maintenance of composite systems are also attracting attention. However, applying PHM techniques to a composite system is challenging due to the data imbalance problem from the lack of failure data and unpredictable failure cases. Despite numerous studies conducted to address this limitation, including techniques like data augmentation and transfer learning, significant challenges remain. In this study, the Wasserstein Generative Adversarial Network (WGAN) model using a time-series data augmentation technique is proposed as a solution to the data imbalance problem. To ensure the performance of the WGAN model, time-series data augmentation of experimental data is executed with a frequency analysis. After that, a One-Dimensional Convolutional Neural Network (1D CNN) is used for fault diagnosis in laminated composites, validating the performance improvement after data augmentation. The proposed data augmentation significantly elevated the performance of the 1D CNN classification model compared to its non-augmented counterpart. Specifically, the accuracy increased from 89.20% to 91.96%. The precision improved remarkably from 29.76% to 74.10%, and its sensitivity rose from 33.33% to 94.39%. Collectively, these enhancements highlight the vital role of data augmentation in improving fault diagnosis performance. P 2023 11837  
unseen defects in the interior of the structure, and using the vibration data demonstrates high fault diagnosis performance [13; 14; 15; 16]. Initially, conventional Machine Learning (ML) model-based fault diagnosis was mainly used, in which engineers manually extracted features related to the faults of the system and diagnosed the condition of the system [17]. Features extracted from signals are generally from the time domain, frequency domain, and time-frequency domain [18]. Time domain features vary from conventional values, for example, the mean, standard deviation, peak, amplitude square, and Root Mean Square (RMS), to combinations of these features, like the waveform factor, peak factor, margin factor, and central moment [19; 20]. For more detailed information, frequency, as well as time-frequency domain features, could also be derived from the Fourier transform, short-time Fourier transform, wavelet transform, and wavelet packet transform [21; 22; 23]. Because features related to frequency analysis contain the dynamic behavior of the system, they can offer more sensitive and thorough information about the system. The extracted features are then utilized to predict whether the system is in a normal or fault state. ML models, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree, are trained by part of the extracted features, while the rest of the features are exploited to test whether the trained model can properly decide the health state of the system [24; 25; 26]. Although fault diagnosis using ML showed remarkable performance, it was time consuming, and a massive theoretical background was needed to extract features manually from the data. To overcome this limitation, Deep Learning (DL) techniques started to be utilized for fault diagnosis. The DL model can extract features automatically by using its neural network and classify the health state of the system. The Convolutional Neural Network (CNN) model is generally used for fault diagnosis owing to its outstanding feature extraction performance from motor current signals, vibration signals, and multi-variate signals [27; 28; 29; 30]. Raw signals are converted into images that can represent the health state of the system, like spectrogram, scalogram, or various sorts of grey-scale images. The CNN model extracts features from these images and then predicts whether the system is in a normal state. The Vision Transformer (ViT) model is also utilized for fault diagnosis using image data [31].