… data augmentation scheme for machine fault diagnosis based on an SC-GAN that contains
a two-stage training process is developed. The scheme … -quality raw vibration data without a …

… synthetic data augmentation through virtual sensors for the deep learning-based fault
diagnosis of a rotating machine with 42 different classes. The original and augmented data were …

… a hybrid method of data augmentation to increase the number of … For comparison purposes,
four machine learning … is applied for fault detection and classification in rotating machines. …

… The inputs are different so we consider two ANN structures for the machine fault detection
application. The first ANN model for the full FFT of the signal has a large structure because the …

… To address this problem, this paper proposes a feature-level consistency regularized
semi-supervised scheme with data augmentation for fault diagnosis of machines. In the proposed …

… data augmentation (DA) and metric learning is proposed for an intelligent bearing fault diagnosis
under limited labeled data… on an experimental bearing fault dataset from our laboratory …

… accuracy of fault diagnosis. Moreover, … fault diagnosis, which is the basis for building a new
model. Secondly, we introduce the GAN data augmentation, and the generated training data …

… built to generate auxiliary data for the low-data original dataset in industrial process for fault
diagnosis. Since the focus of this paper is the ability of GAN based data augmentation, the …

… Therefore, the conditional GAN (CGAN) 19 is first used in this paper to generate vibration
signals for data augmentation of all the fault types in one time. In addition, spectrum …

… samples for imbalanced fault data with normal data and less faulty data. The experiment …
[21] proposed a novel fault diagnosis model based on data augmentation techniques called …