Improved Generative Adversarial Network for Bearing Fault Diagnosis with a Small Number of Data and Unbalanced Data

Abstract

Fault data under real operating conditions are often difficult to collect, making the number of trained fault data small and out of proportion to normal data. Thus, fault diagnosis symmetry (balance) is compromised. This will result in less effective fault diagnosis methods for cases with a small number of data and data imbalances (S&I). We present an innovative solution to overcome this problem, which is composed of two components: data augmentation and fault diagnosis. In the data augmentation section, the S&I dataset is supplemented with a deep convolutional generative adversarial network based on a gradient penalty and Wasserstein distance (WDCGAN-GP), which solve the problems of the generative adversarial network (GAN) being prone to model collapse and the gradient vanishing during the training time. The addition of self-attention allows for a better identification and generation of sample features. Finally, the addition of spectral normalization can stabilize the training of the model. In the fault diagnosis section, fault diagnosis is performed through a convolutional neural network with coordinate attention (CNN-CA). Our experiments conducted on two bearing fault datasets for comparison demonstrate that the proposed method surpasses other comparative approaches in terms of the quality of data augmentation and the accuracy of fault diagnosis. It effectively addresses S&I fault diagnosis challenges.

Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network

“…Lu et al proposed an Improved Active Learning (IAL) diagnostic method for the intelligent labelling of unlabelled samples with a limited number of labelled samples, showing that IAL can significantly improve the classification of unbalanced data [15]. Qin et al proposed an IGAN method for bearing fault diagnosis for a small dataset and unbalanced dataset that combines the coordinate attention mechanism to effectively mine information from a limited number of fault samples, thus increasing the diagnosis accuracy [16]. Wei et al proposed an improved channel-attention CNN for the fault diagnosis of rolling bearings; this method can be better used for the feature extraction of unbalanced data compared to other shallow models [17].…”