| Artificial intelligence for fault diagnosis of rotating machinery: A review R Liu, B Yang, E Zio, X Chen Mechanical Systems and Signal Processing 108, 33-47, 2018 | 1778 | 2018 |
| Wavelets for Fault Diagnosis of Rotary Machines: A Review with Applications R Yan, RX Gao, X Chen Signal Processing 96, 1-15, 2014 | 1377 | 2014 |
| 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 | 736 | 2016 |
| 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, 2018 | 412 | 2018 |
| 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 | 340 | 2016 |
| Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings Z Liu, H Cao, X Chen, Z He, Z Shen Neurocomputing, 2012 | 321 | 2012 |
| Mechanical model development of rolling bearing-rotor systems: A review H Cao, L Niu, S Xi, X Chen Mechanical Systems and Signal Processing 102, 37-58, 2018 | 290 | 2018 |
| Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis W Sun, R Zhao, R Yan, S Shao, X Chen IEEE Transactions on Industrial Informatics 13 (3), 1350-1359, 2017 | 288 | 2017 |
| The concept and progress of intelligent spindles: a review H Cao, X Zhang, X Chen International Journal of Machine Tools and Manufacture 112, 21-52, 2017 | 286 | 2017 |
| New clustering algorithm-based fault diagnosis using compensation distance evaluation technique Y Lei, Z He, Y Zi, X Chen Mechanical Systems and Signal Processing 22 (2), 419-435, 2008 | 285 | 2008 |
| Matching demodulation transform and synchrosqueezing in time-frequency analysis S Wang, X Chen, G Cai, B Chen, X Li, Z He IEEE Transactions on Signal Processing 62 (1), 169-84, 2014 | 264 | 2014 |
| 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 | 244 | 2018 |
| Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis J Tan, X Chen, J Wang, H Chen, H Cao, Y Zi, Z He Mechanical systems and signal processing 23 (3), 811-822, 2009 | 222 | 2009 |
| The construction of wavelet finite element and its application X Chen, S Yang, J Ma, Z He Finite Elements in Analysis and Design 40 (5-6), 541-554, 2004 | 217 | 2004 |
| Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox G Cai, X Chen, Z He Mechanical Systems and Signal Processing 41 (1), 34-53, 2013 | 216 | 2013 |
| Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD B Yang, R Liu, X Chen IEEE Transactions on Industrial Informatics 13 (3), 1321-1331, 2017 | 203 | 2017 |
| An ACO-based algorithm for parameter optimization of support vector machines XL Zhang, XF Chen, ZJ He Expert Systems with Applications 37 (9), 6618-6628, 2010 | 202 | 2010 |
| Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis S Wang, I Selesnick, G Cai, Y Feng, X Sui, X Chen IEEE Transactions on Industrial Electronics 65 (9), 7332-7342, 2018 | 197 | 2018 |
| 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 | 187 | 2018 |
| Wavelet-based numerical analysis: A review and classification B Li, X Chen Finite Elements in Analysis and Design 81, 14-31, 2014 | 187 | 2014 |
xingwu zhangInstructor of Mechanical Engineering, Xi'an Jiaotong UniversityDirección de correo verificada de mail.xjtu.edu.cn
