SCLIFD combines supervised contrastive learning, feature-space distillation, marginal exemplar replay, and a balanced random forest to improve class-incremental fault diagnosis with few fault samples.
Multi-gate mixture-of- expert combined with synthetic minority over-sampling technique for multimode imbalanced fault diagnosis,
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Class Incremental Fault Diagnosis under Limited Fault Data via Supervised Contrastive Knowledge Distillation
SCLIFD combines supervised contrastive learning, feature-space distillation, marginal exemplar replay, and a balanced random forest to improve class-incremental fault diagnosis with few fault samples.