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Ruqiang Yan

2004–2026 年に発表

267
論文数
27,049
被引用数
72
h 指数
185
i10 指数

被引用数

Ruqiang Yan の年別被引用数1962 年: 被引用 1 件1977 年: 被引用 1 件1997 年: 被引用 2 件2000 年: 被引用 3 件2002 年: 被引用 1 件2003 年: 被引用 3 件2004 年: 被引用 2 件2005 年: 被引用 2 件2006 年: 被引用 10 件2007 年: 被引用 3 件2008 年: 被引用 8 件2009 年: 被引用 15 件2010 年: 被引用 22 件2011 年: 被引用 19 件2012 年: 被引用 50 件2013 年: 被引用 61 件2014 年: 被引用 85 件2015 年: 被引用 116 件2016 年: 被引用 141 件2017 年: 被引用 198 件2018 年: 被引用 350 件2019 年: 被引用 698 件2020 年: 被引用 1,065 件2021 年: 被引用 1,353 件2022 年: 被引用 1,628 件2023 年: 被引用 1,940 件2024 年: 被引用 2,191 件2025 年: 被引用 1,969 件2026 年: 被引用 425 件2027 年: 被引用 7 件1963〜1976 年は被引用が無いため表示していません1978〜1996 年は被引用が無いため表示していません1998〜1999 年は被引用が無いため表示していません2001 年は被引用が無いため表示していません

引用元

国・地域

この著者を引用した国・地域の世界地図中国: 引用元論文 5,933 件、この内訳の 51.8%アメリカ合衆国: 引用元論文 733 件、この内訳の 6.4%イギリス: 引用元論文 468 件、この内訳の 4.1%インド: 引用元論文 400 件、この内訳の 3.5%カナダ: 引用元論文 328 件、この内訳の 2.9%韓国: 引用元論文 264 件、この内訳の 2.3%イタリア: 引用元論文 206 件、この内訳の 1.8%シンガポール: 引用元論文 200 件、この内訳の 1.7%香港: 引用元論文 194 件、この内訳の 1.7%ドイツ: 引用元論文 191 件、この内訳の 1.7%オーストラリア: 引用元論文 179 件、この内訳の 1.5%フランス: 引用元論文 155 件、この内訳の 1.3%
0%51.8%その他 19.3%

機関

分野

  • Engineering67.5%
  • Computer Science19.6%
  • Medicine4.4%
  • Neuroscience1.7%
  • Environmental Science0.9%
  • Health Professions0.9%
  • その他5%

トピック

  • Machine Fault Diagnosis Techniques16.7%
  • Fault Detection and Control Systems8.9%
  • Gear and Bearing Dynamics Analysis5%
  • Anomaly Detection Techniques and Applications4.3%
  • Engineering Diagnostics and Reliability3.5%
  • Industrial Vision Systems and Defect Detection2.4%
  • その他59.2%

共著者

全論文

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  1. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

    著者: Siyu Shao, Stephen McAleer, Ruqiang Yan, Pierre Baldi - IEEE Transactions on Industrial Informatics, IEEE Trans. Ind. Informatics 2018 被引用: 1,561

  2. Deep learning and its applications to machine health monitoring

    著者: Rui Zhao, Ruqiang Yan, Zhenghua Chen, Kezhi Mao, Peng Wang, Robert X. Gao - Mechanical Systems and Signal Processing 2018 被引用: 2,644

  3. A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

    著者: Weihua Li, Ruyi Huang, Jipu Li, Yixiao Liao, Zhuyun Chen, Guolin He, Ruqiang Yan, Konstantinos Gryllias - Mechanical Systems and Signal Processing 2021 被引用: 779

  4. The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study

    著者: Tianfu Li, Zheng Zhou, Sinan Li, Chuang Sun, Ruqiang Yan, Xuefeng Chen - Mechanical Systems and Signal Processing 2021 被引用: 609

  5. Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach

    著者: Zhenghua Chen, Min Wu, Rui Zhao, Feri Guretno, Ruqiang Yan, Xiaoli Li - IEEE Transactions on Industrial Electronics, IEEE Trans. Ind. Electron. 2020 被引用: 580

  6. Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

    著者: Zhibin Zhao, Tianfu Li, Jingyao Wu, Chuang Sun, Shibin Wang, Ruqiang Yan, Xuefeng Chen - ISA Transactions 2020 被引用: 634

  7. Multireceptive Field Graph Convolutional Networks for Machine Fault Diagnosis

    著者: Tianfu Li, Zhibin Zhao, Chuang Sun, Ruqiang Yan, Xuefeng Chen - IEEE Transactions on Industrial Electronics, IEEE Trans. Ind. Electron. 2020 被引用: 370

  8. Wavelet transform for rotary machine fault diagnosis:10 years revisited

    著者: Ruqiang Yan, Zuogang Shang, Hong Xu, Jingcheng Wen, Zhibin Zhao, Xuefeng Chen, Robert X. Gao - Mechanical Systems and Signal Processing 2023 被引用: 260

  9. Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction

    著者: Tianfu Li, Zhibin Zhao, Chuang Sun, Ruqiang Yan, Xuefeng Chen - Reliability Engineering & System Safety, Reliab. Eng. Syst. Saf. 2021 被引用: 233

  10. Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

    著者: Rui Zhao, Dongzhe Wang, Ruqiang Yan, Kezhi Mao, Fei Shen, Jinjiang Wang - IEEE Transactions on Industrial Electronics, IEEE Trans. Ind. Electron. 2017 被引用: 874

  11. Generative adversarial networks for data augmentation in machine fault diagnosis

    著者: Siyu Shao, Pu Wang, Ruqiang Yan - Computers in Industry, Comput. Ind. 2019 被引用: 542

  12. Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions

    著者: Tianfu Li, Zhibin Zhao, Chuang Sun, Ruqiang Yan, Xuefeng Chen - IEEE Transactions on Instrumentation and Measurement, IEEE Trans. Instrum. Meas. 2021 被引用: 273

  13. CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery

    著者: Yadong Xu, Ke Feng, Xiaoan Yan, Ruqiang Yan, Qing Ni, Beibei Sun, Zihao Lei, Yongchao Zhang, Zheng Liu - Information Fusion, Inf. Fusion 2023 被引用: 173

  14. Defect-aware transformer network for intelligent visual surface defect detection

    著者: Hongbing Shang, Chuang Sun, Jinxin Liu, Xuefeng Chen, Ruqiang Yan - Advanced Engineering Informatics, Adv. Eng. Informatics 2023 被引用: 148

  15. LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification

    著者: Borui Hou, Jianyong Yang, Pu Wang, Ruqiang Yan - IEEE Transactions on Instrumentation and Measurement, IEEE Trans. Instrum. Meas. 2019 被引用: 301

  16. Wavelets for fault diagnosis of rotary machines: A review with applications

    著者: Ruqiang Yan, Robert X. Gao, Xuefeng Chen - Signal Processing, Signal Process. 2013 被引用: 1,406

  17. DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

    著者: Siyu Shao, Ruqiang Yan, Yadong Lu, Peng Wang, Robert X. Gao - IEEE Transactions on Instrumentation and Measurement, IEEE Trans. Instrum. Meas. 2019 被引用: 426

  18. Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing

    著者: Chuang Sun, Meng Ma, Zhibin Zhao, Shaohua Tian, Ruqiang Yan, Xuefeng Chen - IEEE Transactions on Industrial Informatics, IEEE Trans. Ind. Informatics 2018 被引用: 523

  19. Variational Attention-Based Interpretable Transformer Network for Rotary Machine Fault Diagnosis

    著者: Yasong Li, Zheng Zhou, Chuang Sun, Xuefeng Chen, Ruqiang Yan - IEEE Transactions on Neural Networks and Learning Systems, IEEE Trans. Neural Networks Learn. Syst. 2022 被引用: 146

  20. Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction

    著者: Mohamed Ragab, Zhenghua Chen, Min Wu, Chuan Sheng Foo, Chee Keong Kwoh, Ruqiang Yan, Xiaoli Li - IEEE Transactions on Industrial Informatics, IEEE Trans. Ind. Informatics 2020 被引用: 188

  21. Few-shot transfer learning for intelligent fault diagnosis of machine

    著者: Jingyao Wu, Zhibin Zhao, Chuang Sun, Ruqiang Yan, Xuefeng Chen - Measurement 2020 被引用: 299

  22. Multilayer Grad-CAM: An effective tool towards explainable deep neural networks for intelligent fault diagnosis

    著者: Sinan Li, Tianfu Li, Chuang Sun, Ruqiang Yan, Xuefeng Chen - Journal of Manufacturing Systems 2023 被引用: 108

  23. Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

    著者: Rui Zhao, Ruqiang Yan, Jinjiang Wang, Kezhi Mao - Sensors 2017 被引用: 734

  24. WPConvNet: An Interpretable Wavelet Packet Kernel-Constrained Convolutional Network for Noise-Robust Fault Diagnosis

    著者: Sinan Li, Tianfu Li, Chuang Sun, Xuefeng Chen, Ruqiang Yan - IEEE Transactions on Neural Networks and Learning Systems, IEEE Trans. Neural Networks Learn. Syst. 2023 被引用: 84