| A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox L Jing, M Zhao, P Li, X Xu Measurement 111, 1-10, 2017 | 750 | 2017 |
| A comprehensive review on convolutional neural network in machine fault diagnosis J Jiao, M Zhao, J Lin, K Liang Neurocomputing 417, 36-63, 2020 | 482 | 2020 |
| An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox L Jing, T Wang, M Zhao, P Wang Sensors 17 (2), 414, 2017 | 403 | 2017 |
| Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings Y Miao, M Zhao, J Lin, Y Lei Mechanical Systems and Signal Processing 92, 173-195, 2017 | 344 | 2017 |
| A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery M Zhao, X Jia Mechanical Systems and Signal Processing 94, 129-147, 2017 | 280 | 2017 |
| Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition Y Miao, M Zhao, J Lin ISA transactions 84, 82-95, 2019 | 241 | 2019 |
| Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification Y Miao, M Zhao, J Lin Measurement Science and Technology 28 (12), 125001, 2017 | 235 | 2017 |
| Residual joint adaptation adversarial network for intelligent transfer fault diagnosis J Jiao, M Zhao, J Lin, K Liang Mechanical Systems and Signal Processing 145, 106962, 2020 | 190 | 2020 |
| A tacho-less order tracking technique for large speed variations M Zhao, J Lin, X Wang, Y Lei, J Cao Mechanical Systems and Signal Processing 40 (1), 76-90, 2013 | 182 | 2013 |
| A review on the application of blind deconvolution in machinery fault diagnosis Y Miao, B Zhang, J Lin, M Zhao, H Liu, Z Liu, H Li Mechanical Systems and Signal Processing 163, 108202, 2022 | 177 | 2022 |
| Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis X Xu, M Zhao, J Lin, Y Lei Measurement 91, 385-397, 2016 | 173 | 2016 |
| Deep coupled dense convolutional network with complementary data for intelligent fault diagnosis J Jiao, M Zhao, J Lin, C Ding IEEE Transactions on Industrial Electronics 66 (12), 9858-9867, 2019 | 172 | 2019 |
| Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds M Zhao, J Lin, X Xu, Y Lei Sensors 13 (8), 10856-10875, 2013 | 172 | 2013 |
| Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis D He, X Wang, S Li, J Lin, M Zhao Mechanical Systems and Signal Processing 81, 235-249, 2016 | 154 | 2016 |
| Unsupervised adversarial adaptation network for intelligent fault diagnosis J Jiao, M Zhao, J Lin IEEE Transactions on Industrial Electronics 67 (11), 9904-9913, 2019 | 149 | 2019 |
| Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings M Zhao, J Lin, Y Miao, X Xu Measurement 91, 421-439, 2016 | 146 | 2016 |
| Health assessment of rotating machinery using a rotary encoder M Zhao, J Lin IEEE Transactions on Industrial Electronics 65 (3), 2548-2556, 2017 | 138 | 2017 |
| A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes J Jiao, M Zhao, J Lin, J Zhao Knowledge-Based Systems 160, 237-250, 2018 | 126 | 2018 |
| Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox M Zhao, X Jia, J Lin, Y Lei, J Lee Mechanical Systems and Signal Processing 98, 16-31, 2018 | 125 | 2018 |
| Assessment of data suitability for machine prognosis using maximum mean discrepancy X Jia, M Zhao, Y Di, Q Yang, J Lee IEEE transactions on industrial electronics 65 (7), 5872-5881, 2017 | 123 | 2017 |
Jinyang JiaoAssociate Professor, Beihang UniversityE-mailová adresa ověřena na: buaa.edu.cn