Semisupervised Fault Diagnosis for Gearboxes: A Novel Method Based on a Hybrid Classification Network and Weighted Pseudo-Labeling

To make the gearboxes reliable and secure, accurate and efficient fault diagnosis has received wide attention. Recently, many data-driven intelligent methods have made great progress due to their powerful feature extraction capability. However, most deep learning (DL)-based methods can only perform well with sufficient labeled data and the shortage of labeled data has become a common problem in practical industrial applications. Therefore, a semisupervised approach based on a hybrid classification network and weighted pseudo-labeling (HCN-WPL) is proposed to relieve the above problem. The hybrid classification network (HCN) is composed of an autoencoder and a softmax classifier. With this structure, the process of latent representation learning of autoencoder is guided by the supervised classification training. Therefore, the features extracted by the autoencoder are more suitable for the classification task. Moreover, a weighted pseudo-labeling (PL) method is developed to further enhance the generalization capability of the model. First, pseudo-labels for the unlabeled samples are generated according to the model predictions. Then, the proposed sample weighting scheme assigns a confidence-based weight to each pseudo-labeled sample, which is used to filter out the incorrect pseudo-labels. Finally, these weighted pseudo-labeled samples are utilized to further optimize the model. Two gearbox datasets are utilized to validate the effectiveness of the proposed method. One is an experimental gearbox dataset and the other is an industrial wind turbine gearbox dataset. The experimental results show that the proposed method can still achieve high diagnostic accuracies for both the experimental and industrial datasets under limited labeled data.