Semisupervised Fault Diagnosis of Gearbox Using Weighted Graph-Based Label Propagation and Virtual Adversarial Training

Fault diagnosis of gearbox is essential for the safe operation of the modern industrial system. Recently, supervised deep learning (DL)-based approaches have rapidly developed because they can achieve an automated fault-sensitive feature extraction process. However, most DL-based methods work on the assumption that labeled data are sufficient, and the field data recorded with label information are difficult to obtain in practical industrial applications. Moreover, there is a huge amount of unlabeled data that are collected in industrial practice and they can be utilized by semisupervised learning-based methods to enhance the identification ability of the model. Therefore, a novel semisupervised method for gearboxes using weighted label propagation and virtual adversarial training (WLP-VAT) is proposed in this article. In this study, the transductive label propagation method is introduced to infer pseudo labels for the unlabeled samples, which is based on the assumption that similar samples are likely to have the same labels. Meanwhile, a sample weight reflecting the uncertainty of each pseudo label is proposed to reduce the negative effect caused by noisy labels. Moreover, VAT is adopted to further enhance the identification capability of the model under limited labeled data. The effectiveness of the proposed semisupervised approach is verified by an experimental gearbox dataset and a wind turbine gearbox dataset. The results of the above two experiments indicate that our proposed semisupervised can leverage unlabeled data to solve the labeled data shortage.

Automated summary

Fault diagnosis of gearboxes is crucial for the safe operation of industrial systems. This article proposes a novel semisupervised method for gearboxes using weighted label propagation and virtual adversarial training to overcome the shortage of labeled data in practical industrial applications. The proposed method leverages unlabeled data for pseudo label inference and introduces sample weights to reduce the negative effect of noisy labels. Experimental results demonstrate the effectiveness of the proposed semisupervised approach in leveraging unlabeled data to solve the labeled data shortage.