| Deep residual shrinkage networks for fault diagnosis M Zhao, S Zhong, X Fu, B Tang, M Pecht IEEE Transactions on Industrial Informatics 16 (7), 4681-4690, 2020 | 812 | 2020 |
| Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes M Zhao, M Kang, B Tang, M Pecht IEEE Transactions on Industrial Electronics 65 (5), 4290-4300, 2018 | 369 | 2018 |
| Multiple wavelet coefficients fusion in deep residual networks for fault diagnosis M Zhao, M Kang, B Tang, M Pecht IEEE Transactions on Industrial Electronics 66 (6), 4696-4706, 2019 | 161 | 2019 |
| Deep residual networks with adaptively parametric rectifier linear units for fault diagnosis M Zhao, S Zhong, X Fu, B Tang, S Dong, M Pecht IEEE Transactions on Industrial Electronics 68 (3), 2587-2597, 2020 | 152 | 2020 |
| Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction M Zhao, B Tang, Q Tan Measurement 86, 41-55, 2016 | 103 | 2016 |
| Self-attention ConvLSTM and its application in RUL prediction of rolling bearings B Li, B Tang, L Deng, M Zhao IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2021 | 74 | 2021 |
| Deep residual LSTM with domain-invariance for remaining useful life prediction across domains S Fu, Y Zhang, L Lin, M Zhao, S Zhong Reliability Engineering & System Safety 216, 108012, 2021 | 69 | 2021 |
| Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks M Zhao, X Fu, Y Zhang, L Meng, B Tang Advanced Engineering Informatics 51, 101535, 2022 | 53 | 2022 |
| Highly imbalanced fault diagnosis of gas turbines via clustering-based downsampling and deep siamese self-attention network D Liu, S Zhong, L Lin, M Zhao, X Fu, X Liu Advanced Engineering Informatics 54, 101725, 2022 | 43 | 2022 |
| A novel time-series memory auto-encoder with sequentially updated reconstructions for remaining useful life prediction S Fu, S Zhong, L Lin, M Zhao IEEE Transactions on Neural Networks and Learning Systems 33 (12), 7114-7125, 2021 | 42 | 2021 |
| A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection S Fu, S Zhong, L Lin, M Zhao Engineering Applications of Artificial Intelligence 101, 104199, 2021 | 35 | 2021 |
| Multiple wavelet regularized deep residual networks for fault diagnosis M Zhao, B Tang, L Deng, M Pecht Measurement 152, 107331, 2020 | 35 | 2020 |
| Regression model for civil aero-engine gas path parameter deviation based on deep domain-adaptation with Res-BP neural network X Zhou, X Fu, M Zhao, S Zhong Chinese Journal of Aeronautics, 2020 | 30 | 2020 |
| An accurate 3-D fire location method based on sub-pixel edge detection and non-parametric stereo matching T Song, B Tang, M Zhao, L Deng Measurement 50, 160-171, 2014 | 30 | 2014 |
| Multi-block domain adaptation with central moment discrepancy for fault diagnosis P Xiong, B Tang, L Deng, M Zhao, X Yu Measurement 169, 108516, 2021 | 29 | 2021 |
| Data Augmentation via Randomized Wavelet Expansion and Its Application in Few-Shot Fault Diagnosis of Aviation Hydraulic Pumps M Zhao, X Fu, Y Zhang, L Meng, S Zhong IEEE Transactions on Instrumentation and Measurement 71, 1-13, 2021 | 22 | 2021 |
| Empirical mode reconstruction: Preserving intrinsic components in data augmentation for intelligent fault diagnosis of civil aviation hydraulic pumps L Meng, M Zhao, Z Cui, X Zhang, S Zhong Computers in Industry 134, 103557, 2022 | 19 | 2022 |
| A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings T Zuo, K Zhang, Q Zheng, X Li, Z Li, G Ding, M Zhao Reliability Engineering & System Safety 237, 109337, 2023 | 18 | 2023 |
| Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix M Zhao, B Tang, Q Tan Measurement Science and Technology 26 (8), 085008, 2015 | 17 | 2015 |
| Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes Q Li, B Tang, L Deng, P Xiong, M Zhao Measurement 200, 111570, 2022 | 16 | 2022 |
Michael G. PechtUniversity of MarylandVerified email at umd.edu
