Intelligent Fault Detection Scheme for Rolling Bearing Based on Generative Adversarial Network and AutoEncoders Using Convolutional Neural Network

Fault detection in early operational stages of rolling bearing is crucial for reliable and safe functioning of rotating machinery. Implementation of intelligent fault detection techniques involving deep learning methods enable automatic feature extraction and selection from raw vibration data provides accurate results. The shortage of enough historical data limits the application of deep learning. Therefore, to solve this problem, in this paper data augmentation method is implemented to generate new data that having greater similitude with the real data for better training of deep learning model for fault detection. For this purpose, WGAN (Wasserstein generative adversarial network) is implemented as imbalanced data augmentation method. Also SAE (stacked autoencoder) is implemented to obtain the latent representation of raw vibration data which is used as noise vector to train WGAN. This has greatly improved the quality of data generation from WGAN. The quality assessment of generated samples is quantified by implementing metrics such as KLD (Kullback–Leibler divergence) and NCC (normalized cross-correlation). The comparison with conventional data generation methods such as VAE, and GAN proves the superior quality of data generation by SAE-WGAN. Test rig experiments are used to gather the vibration data, and deep convolutional neural networks are used to classify the faults (DCNN). The ROC curve and performance evaluation metrics like precision, recall, and F1-score amply demonstrated the excellent discriminative power of the suggested methodology for fault detection. Hence the proposed work successfully implemented as condition monitoring tool for early fault detection in rotating machinery.