| Deep learning based approach for bearing fault diagnosis M He, D He IEEE Transactions on Industry Applications 53 (3), 3057-3065, 2017 | 499 | 2017 |
| A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals M He, D He Neurocomputing 396, 542-555, 2020 | 106 | 2020 |
| Remaining useful life prediction of hybrid ceramic bearings using an integrated deep learning and particle filter approach J Deutsch, M He, D He Applied Sciences 7 (7), 649, 2017 | 101 | 2017 |
| Detection of pitting in gears using a deep sparse autoencoder Y Qu, M He, J Deutsch, D He Applied Sciences 7 (5), 515, 2017 | 69 | 2017 |
| Fast evaluation of aircraft icing severity using machine learning based on XGBoost S Li, J Qin, M He, R Paoli Aerospace 7 (4), 36, 2020 | 57 | 2020 |
| Power consumption estimation for mask image projection stereolithography additive manufacturing using machine learning based approach Y Yang, M He, L Li Journal of cleaner production 251, 119710, 2020 | 50 | 2020 |
| Rolling bearing fault severity identification using deep sparse auto-encoder network with noise added sample expansion R Chen, S Chen, M He, D He, B Tang Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2017 | 40 | 2017 |
| A new method to classify railway vehicle axle fatigue crack AE signal Y Zhou, L Lin, D Wang, M He, D He Applied Acoustics 131, 174-185, 2018 | 35 | 2018 |
| Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach M He, D He, J Yoon, TJ Nostrand, J Zhu, E Bechhoefer Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2019 | 30 | 2019 |
| Gear pitting fault diagnosis using disentangled features from unsupervised deep learning Y Qu, Y Zhang, M He, D He, C Jiao, Z Zhou Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2019 | 28 | 2019 |
| Experimental study of dynamic strain for gear tooth using fiber Bragg gratings and piezoelectric strain sensors Y Qu, L Hong, X Jiang, M He, D He, Y Tan, Z Zhou Proceedings of the Institution of Mechanical Engineers, Part C: Journal of …, 2018 | 23 | 2018 |
| Using deep learning based approaches for bearing fault diagnosis with AE sensors M He, D He, E Bechhoefer Annual Conference of the PHM Society 8 (1), 2016 | 16 | 2016 |
| A new machine learning based geometry feature extraction approach for energy consumption estimation in mask image projection stereolithography Y Yang, M He, L Li Procedia CIRP 80, 741-745, 2019 | 14 | 2019 |
| A new signal processing and feature extraction approach for bearing fault diagnosis using AE sensors M He, D He, Y Qu Journal of Failure Analysis and Prevention 16, 821-827, 2016 | 11 | 2016 |
| Simultaneous bearing fault diagnosis and severity detection using a LAMSTAR network‐based approach M He, D He IET Science, Measurement & Technology 12 (7), 893-901, 2018 | 7 | 2018 |
| A natural language processing based planetary gearbox fault diagnosis with acoustic emission signals D He, M He, J Yoon 2023 IEEE Aerospace Conference, 01-06, 2023 | 3 | 2023 |
| Few-Shot Learning for Full Ceramic Bearing Fault Diagnosis with Acoustic Emission Signals D He, M He, A Taffari PHM Society Asia-Pacific Conference 4 (1), 2023 | 2 | 2023 |
| A regularized deep clustering method for fault trend analysis Y Qu, Y Zhang, D He, M He, Z Zhou Annual Conference of the PHM Society 11 (1), 2019 | 2 | 2019 |
| Few-shot Learning for Plastic Bearing Fault Diagnosis–An Integrated Image Processing and NLP Approach D He, M He Annual Conference of the PHM Society 15 (1), 2023 | 1 | 2023 |
| Analysis of a Diesel Engine Exhaust Manifold Y Yang, M He, M Mojtahed ASME International Mechanical Engineering Congress and Exposition 46583 …, 2014 | 1 | 2014 |
