| Deep transfer learning based on sparse autoencoder 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, 2018 | 505 | 2018 |
| 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 | 489 | 2020 |
| 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, 2021 | 386 | 2021 |
| Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine R Liu, G Meng, B Yang, C Sun, X Chen IEEE Transactions on Industrial Informatics 13 (3), 1310-1320, 2016 | 370 | 2016 |
| 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 | 315 | 2022 |
| Deep coupling autoencoder for fault diagnosis with multimodal sensory data M Ma, C Sun, X Chen IEEE Transactions on Industrial Informatics 14 (3), 1137-1145, 2018 | 278 | 2018 |
| Multireceptive field graph convolutional networks for machine fault diagnosis T Li, Z Zhao, C Sun, R Yan, X Chen IEEE Transactions on Industrial Electronics 68 (12), 12739-12749, 2020 | 274 | 2020 |
| Few-shot transfer learning for intelligent fault diagnosis of machine J Wu, Z Zhao, C Sun, R Yan, X Chen Measurement 166, 108202, 2020 | 270 | 2020 |
| Domain adversarial graph convolutional network for fault diagnosis under variable working conditions T Li, Z Zhao, C Sun, R Yan, X Chen IEEE Transactions on Instrumentation and Measurement 70, 1-10, 2021 | 240 | 2021 |
| Knowledge transfer for rotary machine fault diagnosis R Yan, F Shen, C Sun, X Chen IEEE Sensors Journal 20 (15), 8374-8393, 2019 | 231 | 2019 |
| Sparse deep stacking network for fault diagnosis of motor C Sun, M Ma, Z Zhao, X Chen IEEE Transactions on Industrial Informatics 14 (7), 3261-3270, 2018 | 201 | 2018 |
| Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine X Chen, Z Shen, Z He, C Sun, Z Liu Proceedings of the Institution of Mechanical Engineers, Part C: Journal of …, 2013 | 184 | 2013 |
| Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction T Li, Z Zhao, C Sun, R Yan, X Chen Reliability Engineering & System Safety 215, 107878, 2021 | 150 | 2021 |
| Discriminative deep belief networks with ant colony optimization for health status assessment of machine M Ma, C Sun, X Chen IEEE Transactions on Instrumentation and Measurement 66 (12), 3115-3125, 2017 | 142 | 2017 |
| Fault-attention generative probabilistic adversarial autoencoder for machine anomaly detection J Wu, Z Zhao, C Sun, R Yan, X Chen IEEE Transactions on Industrial Informatics 16 (12), 7479-7488, 2020 | 123 | 2020 |
| Explainable convolutional neural network for gearbox fault diagnosis J Grezmak, P Wang, C Sun, RX Gao Procedia CIRP 80, 476-481, 2019 | 102 | 2019 |
| A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time Z Shen, Z He, X Chen, C Sun, Z Liu Sensors 12 (8), 10109-10135, 2012 | 92 | 2012 |
| Defect-aware transformer network for intelligent visual surface defect detection H Shang, C Sun, J Liu, X Chen, R Yan Advanced Engineering Informatics 55, 101882, 2023 | 75 | 2023 |
| Research on bearing life prediction based on support vector machine and its application C Sun, Z Zhang, Z He Journal of Physics: Conference Series 305 (1), 012028, 2011 | 67 | 2011 |
| Adaptive Channel Weighted CNN with Multi-Sensor Fusion for Condition Monitoring of Helicopter Transmission System T Li, Z Zhao, C Sun*, R Yan, X Chen IEEE Sensors Journal, 2020 | 63 | 2020 |
Ruqiang YanXJTUVerified email at ieee.org