Standout Papers

A systematic review of deep transfer learning for machinery fault diagnosis 2020 2026 2022 2024336
  1. A systematic review of deep transfer learning for machinery fault diagnosis (2020)
    Chuan Li, Shaohui Zhang et al. Neurocomputing

Immediate Impact

68 standout
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Citing Papers

Data-driven machinery fault diagnosis: A comprehensive review
2025 Standout
Deep learning for cerebral vascular occlusion segmentation: A novel ConvNeXtV2 and GRN-integrated U-Net framework for diffusion-weighted imaging
2025 Standout
39 intermediate papers

Works of Qin Yi being referenced

A systematic review of deep transfer learning for machinery fault diagnosis
2020 Standout
ReLTanh: An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis
2019
and 3 more

Author Peers

Author Last Decade Papers Cites
Qin Yi 468 65 146 325 17 692
Shanshan Ji 442 49 142 282 17 581
Zexian Wei 549 72 192 371 24 754
Zhiyu Zhu 516 60 160 339 30 790
Yiyao An 663 70 213 294 16 802
Junbin Chen 425 53 120 214 21 672
Xingkai Yang 625 62 190 428 24 833
Jichao Zhuang 545 73 155 353 22 761
Li Pin 573 66 209 424 18 783
Xiaoyang Liu 602 76 214 423 16 814
Ke Li 480 60 174 247 21 613

All Works

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2026