Standout Papers

Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks 2017 2026 2020 2023 517
  1. Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks (2017)
    Min Xia, Teng Li et al. IEEE/ASME Transactions on Mechatronics
  2. Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images (2020)
    Haidong Shao, Min Xia et al. IEEE Transactions on Industrial Informatics
  3. Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning (2021)
    Min Xia, Haidong Shao et al. Reliability Engineering & System Safety
  4. Multi-scale deep intra-class transfer learning for bearing fault diagnosis (2020)
    Xu Wang, Changqing Shen et al. Reliability Engineering & System Safety
  5. A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation (2021)
    Min Xia, Haidong Shao et al. IEEE Transactions on Industrial Informatics
  6. Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery (2021)
    Haidong Shao, Min Xia et al. IEEE/ASME Transactions on Mechatronics
  7. Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions (2021)
    Liang Chen, Qi Li et al. IEEE Transactions on Industrial Informatics
  8. Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions (2023)
    Qi Li, Liang Chen et al. Reliability Engineering & System Safety

Immediate Impact

101 standout
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Citing Papers

Digital twin-inspired methods for rotating machinery intelligent fault diagnosis and remaining useful life prediction: A state-of-the-art review and future challenges
2025 Standout
Study on developing predicted system model of cutting-edge trajectory for micro-milling process based on tool runout error, chip thickness and force signal
2025 Standout
89 intermediate papers

Works of Min Xia being referenced

Digital twin-assisted intelligent fault diagnosis for bearings
2024
Side-Milling-Force Model Considering Tool Runout and Workpiece Deformation
2023
and 13 more

Author Peers

Author Last Decade Papers Cites
Min Xia 2230 1428 536 798 97 3.8k
Chengliang Liu 1539 1407 628 635 206 5.8k
Jinjiang Wang 1800 1969 929 728 117 5.4k
Long Wen 2714 1542 392 819 65 4.1k
Hao Luo 3336 1282 576 285 174 5.2k
Rui Zhao 2782 2125 738 880 47 5.1k
Clarence W. de Silva 1628 962 594 414 141 3.2k
Yong Qin 2057 1569 547 623 310 5.4k
Zhibin Zhao 3590 2015 820 942 125 5.5k
Jing Lin 3169 2092 345 972 111 4.8k
René–Vinicio Sánchez 2747 1840 266 1028 79 3.7k

All Works

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Rankless by CCL
2026