An Intelligent Fault Diagnosis for Rolling Bearing Based on Adversarial Semi-Supervised Method

Intelligent fault diagnosis of rolling bearing issues have been well addressed with the rapid growth of data scale. However, the performance of most diagnostic algorithms heavily depends on sufficient labeled samples. How to ensure the fault diagnosis accuracy with scarce labeled samples is always a great challenge in real industrial scenarios. To alleviate this issue, a deep adversarial semi-supervised (DASS) method based on the prototype learning network is proposed for rolling bearing fault diagnosis in this study. First, the nine data augmentation methods are investigated to improve the ability of the network to extract discrepant features. Second, a cosine similarity-based classifier architecture is exploited to estimate prototypes of sample features. Third, the cross entropy and the adversarial entropy are introduced to improve diagnostic accuracy and reduce intra-class variation. Extensive experiments on two rolling bearing datasets verify that the proposed method owns a remarkable identification ability and great robustness under scarce labeled samples as training date. At the same time, the diagnostic performance under different number of labeled samples is extensively evaluated in noisy environment with different signal-to-noise ratio.