A generative adversarial network-based data augmentation approach with transient vibration data

Abstract

In order to perform fault classification using machine learning algorithms, sufficient and balanced data is required. The paper presents a novel approach for data augmentation using a Generative Adversarial Network (GAN) with transient, time-dependent vibration data. The proposed approach aims to generate synthetic data in form of spectrograms that closely resemble the characteristics of the real data sets. Synthetic data generation can be used to improve the training performance of neural networks for vibration analysis if not enough or unbalanced real data is available. The authors demonstrate the effectiveness of this approach by training a vibration-based fault detection model using synthetic data and comparing its performance to a model trained on real data only as well as with at-distributed stochastic neighbour embedding (t-SNE). Real data is acquired on a bearing test rig and measurements are carried out on bearings with four different system states. The results show that the model trained with the synthetic data set outperforms the model trained with real data only, indicating that the synthetic data generated by the proposed approach can improve the training performance and accuracy of the machine learning model. Overall, the paper highlights the potential of GAN-based data augmentation approach via spectrograms for vibration analysis and offers insights into its practical application to bearings.