Vibration indicator-based graph convolutional network for semi-supervised bearing fault diagnosis

Abstract

Since fault diagnosis has entered the big data era, deep learning has been more and more widely studied to diagnose faults of rolling element bearings. Generally, existing methods require labeled data for training before they can be used to recognize faults. However, in real scenarios, massive data are usually unlabeled data rather than labeled ones, because labeling data is always a tough issue and consumes much human labor. In order to fully take advantage of the massive unlabeled data, this paper proposes a vibration indicator-based graph convolutional neural network (VI-GCN) for fault diagnosis. The VI-GCN is applied to a benchmark dataset of bearing faults. Experimental results indicate that it is promising for bearing fault diagnosis when there are few labeled data.