A Novel Domain Adaptive Fault Diagnosis Method for Bearings Based on Unbalance Data Generation

Abstract

Domain adaptive fault diagnosis methods of bearing have made extraordinary achievements in recent years. Among these methods, the vast majority of machine learning problems are balanced classification problems. However, imbalance problems are very common in practical applications. Undersampling and oversampling methods are often used to deal with imbalance problems. Inspired by Wasserstein generative adversarial network (WGAN), a domain adaptive guided WGAN for bearing fault diagnosis under imbalanced samples is proposed in this article. First, the unbalanced data of each bearing fault is collected, and the data and random noise are generated through WGAN to generate a balanced data set, and the processed data is compared with the real and generated bearing signals through the frequency domain feature distribution. Then, stacked autoencoders (SAEs) are used to extract the fault features of the target and source domains and L2 regularization is added in each layer of the fault extractor network to prevent model overfitting. Finally, the domain adaptation guided by maximum mean discrepancy (MMD) is employed to complete the feature alignment of the target and source domains. Therefore, a novel domain adaptive method named WGAN-domain adaptive (DAWGAN) is proposed for imbalanced samples. Two experiments are applied to verify the superiority of the proposed DAWGAN method. The experimental results show that the DAWGAN method can significantly improve the diagnostic accuracy of the bearing, compared with the five comparison methods in this article, the average increase is more than 10%, and it has better stability. Comparison results of two experiments show that the DAWGAN method can significantly improve the diagnosis accuracy of bearing and has better stability than the other methods. Meanwhile, the calculation time is also shortened by an average of 40% compared with the comparison method.