| Deep learning and its applications to machine health monitoring R Zhao, R Yan, Z Chen, K Mao, P Wang, RX Gao Mechanical Systems and Signal Processing 115, 213-237, 2019 | 3219 | 2019 |
| Highly-accurate machine fault diagnosis using deep transfer learning S Shao, S McAleer, R Yan, P Baldi IEEE Transactions on Industrial Informatics 15 (4), 2446-2455, 2019 | 1659 | 2019 |
| Wavelets for fault diagnosis of rotary machines: A review with applications R Yan, RX Gao, X Chen Signal Processing 96, 1-15, 2014 | 1646 | 2014 |
| Machine Health Monitoring Using Local Feature-based Gated Recurrent Unit Networks R Zhao, D Wang, R Yan, K Mao, F Shen, J Wang IEEE Transactions on Industrial Electronics 65 (2), 1539-1548, 2018 | 980 | 2018 |
| Learning to monitor machine health with convolutional bi-directional lstm networks R Zhao, R Yan, J Wang, K Mao Sensors 17 (2), 273, 2017 | 890 | 2017 |
| A sparse auto-encoder-based deep neural network approach for induction motor faults classification W Sun, S Shao, R Zhao, R Yan, X Zhang, X Chen Measurement 89, 171-178, 2016 | 836 | 2016 |
| A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges W Li, R Huang, J Li, Y Liao, Z Chen, G He, R Yan, K Gryllias Mechanical Systems and Signal Processing 167, 108487, 2022 | 832 | 2022 |
| Wavelets: Theory and applications for manufacturing RX Gao, R Yan Springer Science & Business Media, 2010 | 662 | 2010 |
| Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study Z Zhao, T Li, J Wu, C Sun, S Wang, R Yan, X Chen ISA transactions 107, 224-255, 2020 | 657 | 2020 |
| Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study Z Zhao, Q Zhang, X Yu, C Sun, S Wang, R Yan, X Chen IEEE Transactions on Instrumentation and Measurement 70, 1-28, 2021 | 614 | 2021 |
| Machine Remaining Useful Life Prediction via an Attention Based Deep Learning Approach Z Chen, M Wu, R Zhao, F Guretno, R Yan, X Li IEEE Transactions on Industrial Electronics 68 (3), 2521- 2531, 2021 | 613 | 2021 |
| Generative adversarial networks for data augmentation in machine fault diagnosis S Shao, P Wang, R Yan Computers in Industry 106, 85-93, 2019 | 590 | 2019 |
| Deep Transfer Learning Based on Sparse Auto-encoder for Remaining Useful Life Prediction of Tool in Manufacturing C Sun, M Ma, Z Zhao, S Tian, R Yan, X Chen IEEE Transactions on Industrial Informatics 15 (4), 2416-2425, 2019 | 576 | 2019 |
| WaveletKernelNet: An interpretable deep neural network for industrial intelligent diagnosis T Li, Z Zhao, C Sun, L Cheng, X Chen, R Yan, RX Gao IEEE Transactions on Systems, Man, and Cybernetics: Systems 52 (4), 2302-2312, 2022 | 572 | 2022 |
| Approximate entropy as a diagnostic tool for machine health monitoring R Yan, RX Gao Mechanical Systems and Signal Processing 21 (2), 824-839, 2007 | 557 | 2007 |
| The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study T Li, Z Zhou, S Li, C Sun, R Yan, X Chen Mechanical Systems and Signal Processing 168, 108653, 2022 | 554 | 2022 |
| Long short-term memory for machine remaining life prediction J Zhang, P Wang, R Yan, RX Gao Journal of manufacturing systems 48, 78-86, 2018 | 525 | 2018 |
| Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines R Yan, Y Liu, RX Gao Mechanical Systems and Signal Processing 29, 474-484, 2012 | 460 | 2012 |
| DCNN-based Multi-signal Induction Motor Fault Diagnosis S Shao, R Yan, Y Lu, P Wang, R Gao IEEE Transactions on Instrumentation and Measurement 69 (6), 2658-2669, 2020 | 455 | 2020 |
| Hilbert–Huang transform-based vibration signal analysis for machine health monitoring R Yan, RX Gao IEEE Transactions on instrumentation and measurement 55 (6), 2320-2329, 2006 | 441 | 2006 |
Xuefeng CHENProfessor of Xi'an Jiaotong UniversityVerified email at mail.xjtu.edu.cn
