05.09.2026journal-article
Structural Health Monitoring
Semi-supervised weighted stacked autoencoder with spectral peak significance constraints for imbalanced fault diagnosis under low labeled rates
To overcome the severe performance degradation and majority-class bias of traditional fault diagnosis methods under practical scenarios of extreme label scarcity and inherent class imbalance, this paper proposes a semi-supervised weighted stacked autoencoder with spectral peak significance constraints (SSWAEF). A nearest-neighbor consistency voting strategy coupled with a reciprocal-class-size sampling mechanism is first developed to construct a high-quality, class-balanced pseudo-labeled dataset. At the feature le …