| A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals W Zhang, G Peng, C Li, Y Chen, Z Zhang Sensors 17 (2), 425, 2017 | 1271 | 2017 |
| A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load W Zhang, C Li, G Peng, Y Chen, Z Zhang Mechanical systems and signal processing 100, 439-453, 2018 | 1126 | 2018 |
| A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis Z Zhu, G Peng, Y Chen, H Gao Neurocomputing 323, 62-75, 2019 | 322 | 2019 |
| A novel deep learning method based on attention mechanism for bearing remaining useful life prediction Y Chen, G Peng, Z Zhu, S Li Applied Soft Computing 86, 105919, 2020 | 282 | 2020 |
| ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis Y Chen, G Peng, C Xie, W Zhang, C Li, S Liu Neurocomputing 294, 61-71, 2018 | 113 | 2018 |
| Bearing fault diagnosis using fully-connected winner-take-all autoencoder C Li, WEI Zhang, G Peng, S Liu IEEE Access 6, 6103-6115, 2017 | 112 | 2017 |
| Bearings fault diagnosis based on convolutional neural networks with 2-D representation of vibration signals as input W Zhang, G Peng, C Li MATEC web of conferences 95, 13001, 2017 | 112 | 2017 |
| Applying RBR and CBR to develop a VR based integrated system for machining fixture design G Peng, G Chen, C Wu, H Xin, Y Jiang Expert Systems with Applications 38 (1), 26-38, 2011 | 106 | 2011 |
| A desktop virtual reality-based interactive modular fixture configuration design system G Peng, G Wang, W Liu, H Yu Computer-Aided Design 42 (5), 432-444, 2010 | 94 | 2010 |
| Disassembly sequence planning approach for product virtual maintenance based on improved max–min ant system X Liu, G Peng, X Liu, Y Hou The International Journal of Advanced Manufacturing Technology 59, 829-839, 2012 | 62 | 2012 |
| Computer vision algorithm for measurement and inspection of O-rings G Peng, Z Zhang, W Li Measurement 94, 828-836, 2016 | 57 | 2016 |
| Prediction of seal wear with thermal–structural coupled finite element method L Xin, P Gaoliang, L Zhe Finite Elements in Analysis and Design 83, 10-21, 2014 | 50 | 2014 |
| Research on ontology-based integration of product knowledge for collaborative manufacturing Y Jiang, G Peng, W Liu The International Journal of Advanced Manufacturing Technology 49, 1209-1221, 2010 | 45 | 2010 |
| Application of the fiber-optic distributed temperature sensing for monitoring the liquid level of producing oil wells G Peng, J He, S Yang, W Zhou Measurement 58, 130-137, 2014 | 43 | 2014 |
| Rolling element bearings fault intelligent diagnosis based on convolutional neural networks using raw sensing signal W Zhang, G Peng, C Li Advances in Intelligent Information Hiding and Multimedia Signal Processing …, 2017 | 41 | 2017 |
| A two-stage, intelligent bearing-fault-diagnosis method using order-tracking and a one-dimensional convolutional neural network with variable speeds M Ji, G Peng, J He, S Liu, Z Chen, S Li Sensors 21 (3), 675, 2021 | 34 | 2021 |
| A desktop virtual reality‐based integrated system for complex product maintainability design and verification P Gaoliang, H Yu, L Xinhua, J Yang, X He Assembly Automation 30 (4), 333-344, 2010 | 34 | 2010 |
| Dynamic modeling and terminal sliding mode control of a 3-DOF redundantly actuated parallel platform S Liu, G Peng, H Gao Mechatronics 60, 26-33, 2019 | 32 | 2019 |
| Development of a collaborative virtual maintenance environment with agent technology X Liu, G Peng, X Liu, Y Hou Journal of Manufacturing Systems 29 (4), 173-181, 2010 | 30 | 2010 |
| A neural network compression method based on knowledge-distillation and parameter quantization for the bearing fault diagnosis M Ji, G Peng, S Li, F Cheng, Z Chen, Z Li, H Du Applied Soft Computing 127, 109331, 2022 | 28 | 2022 |
