| 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 | 414 | 2018 |
| Deep-convolution-based LSTM network for remaining useful life prediction M Ma, Z Mao IEEE Transactions on Industrial Informatics 17 (3), 1658-1667, 2020 | 277 | 2020 |
| 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 |
| 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 |
| 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 | 128 | 2017 |
| Locally linear embedding on Grassmann manifold for performance degradation assessment of bearings M Ma, X Chen, X Zhang, B Ding, S Wang IEEE Transactions on Reliability 66 (2), 467-477, 2017 | 51 | 2017 |
| A deep coupled network for health state assessment of cutting tools based on fusion of multisensory signals M Ma, C Sun, X Chen, X Zhang, R Yan IEEE Transactions on Industrial Informatics 15 (12), 6415-6424, 2019 | 50 | 2019 |
| Bearing degradation assessment based on weibull distribution and deep belief network M Ma, X Chen, S Wang, Y Liu, W Li 2016 International symposium on flexible automation (ISFA), 382-385, 2016 | 40 | 2016 |
| Deep recurrent convolutional neural network for remaining useful life prediction M Ma, Z Mao 2019 IEEE international conference on prognostics and health management …, 2019 | 26 | 2019 |
| Physics-informed deep neural network for bearing prognosis with multisensory signals X Chen, M Ma, Z Zhao, Z Zhai, Z Mao Journal of Dynamics, Monitoring and Diagnostics, 200-207, 2022 | 24 | 2022 |
| Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery M Ma, C Sun, Z Mao, X Chen ISA transactions 113, 166-174, 2021 | 24 | 2021 |
| Deep wavelet sequence-based gated recurrent units for the prognosis of rotating machinery M Ma, Z Mao Structural Health Monitoring 20 (4), 1794-1804, 2021 | 24 | 2021 |
| Subspace-based MVE for performance degradation assessment of aero-engine bearings with multimodal features M Ma, C Sun, C Zhang, X Chen Mechanical Systems and Signal Processing 124, 298-312, 2019 | 24 | 2019 |
| Deep learning in heterogeneous materials: Targeting the thermo-mechanical response of unidirectional composites Q Chen, W Tu, M Ma Journal of Applied Physics 127 (17), 2020 | 18 | 2020 |
| An improved analytical dynamic model for rotating blade crack: With application to crack detection indicator analysis L Yang, M Ma, S Wu, X Chen, R Yan, Z Mao Journal of Low Frequency Noise, Vibration and Active Control 40 (4), 1935-1961, 2021 | 13 | 2021 |
| Rotating machinery prognostics via the fusion of particle filter and deep learning M Ma, ZHU Mao Structural Health Monitoring 2019, 2019 | 6 | 2019 |
| Fault diagnosis of bearing running status using mutual information M Meng, L Ruonan, H Yushan, C Xuefeng 2014 Prognostics and System Health Management Conference (PHM-2014 Hunan …, 2014 | 5 | 2014 |
| Direct waveform extraction via a deep recurrent denoising autoencoder M Ma, Y Qin, M Haile, Z Mao Nondestructive Characterization and Monitoring of Advanced Materials …, 2019 | 4 | 2019 |
| Dynamic Model-based Digital Twin for Crack Detection of Aeroengine Disk Y Yang, M Ma, Z Zhou, C Sun, R Yan 2021 International Conference on Sensing, Measurement & Data Analytics in …, 2021 | 3 | 2021 |
| Sparsity-aware tight frame learning for rotary machine fault diagnosis H Zhang, X Chen, Z Du, M Ma, X Zhang 2016 IEEE International Instrumentation and Measurement Technology …, 2016 | 2 | 2016 |
