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Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy

Lechang Yang et al.

Journal of Industrial Information Integration2026https://doi.org/10.1016/j.jii.2026.101076article
ABDC C
Weight
0.61

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https://doi.org/https://doi.org/10.1016/j.jii.2026.101076

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@article{lechang2026,
  title        = {{Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy}},
  author       = {Lechang Yang et al.},
  journal      = {Journal of Industrial Information Integration},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jii.2026.101076},
}

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Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy

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Evidence weight

0.61

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.63 × 0.4 = 0.25
M · momentum0.88 × 0.15 = 0.13
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.