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Limited condition monitoring data are recorded with label information in practice, which make the fault identification task more challenging. A semi-supervised learning (SSL) approach can be employed to increase the identification performance of the classifiers under such situation. In this study, a three-stage SSL approach using data augmentation (DA) and metric learning is proposed for an intelligent bearing fault diagnosis under limited labeled data. In the first stage, a DA method comprising seven DA strategies is presented to expand the feature space for the limited labeled samples under each healthy conditions. An optimization objective combining a cross entropy loss and a triplet loss is adopted to enlarge the margin between the feature distributions of limited labeled samples under different healthy conditions. In the second stage, a K-means technique is employed to acquire the cluster centers for the limited …