Contrastive-weighted self-supervised model for long-tailed data classification with vision transformer augmented

•We proposed Contrastive-weighted Self-supervised Model (CSM) for fault classification under long-tailed data.•We composited positive and negative samples for contrastive learning and transfer the encoder to downstream task for final classification.•The imbalanced learning strategy is adopted in the pretext task for improving the training efficiency.•The augmented vision transformer is adopted as the encoder to strengthen the representation learning ability of samples.