An adaptive deep transfer learning method for bearing fault diagnosis Z Wu, H Jiang, K Zhao, X Li Measurement 151, 107227, 2020 | 235 | 2020 |
Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network H Jiang, X Li, H Shao, K Zhao Measurement Science and Technology 29 (6), 065107, 2018 | 158 | 2018 |
Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis K Zhao, H Jiang, K Wang, Z Pei Knowledge-Based Systems 222, 106974, 2021 | 137 | 2021 |
A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings S Haidong, J Hongkai, Z Ke, W Dongdong, L Xingqiu Mechanical Systems and Signal Processing 110, 193-209, 2018 | 106 | 2018 |
Multi-scale integrated deep self-attention network for predicting remaining useful life of aero-engine K Zhao, Z Jia, F Jia, H Shao Engineering Applications of Artificial Intelligence 120, 105860, 2023 | 94 | 2023 |
Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy K Zhao, J Hu, H Shao, J Hu Reliability Engineering & System Safety 236, 109246, 2023 | 75 | 2023 |
A deep transfer maximum classifier discrepancy method for rolling bearing fault diagnosis under few labeled data Z Wu, H Jiang, T Lu, K Zhao Knowledge-Based Systems 196, 105814, 2020 | 73 | 2020 |
A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains K Zhao, F Jia, H Shao Knowledge-Based Systems 262, 110203, 2023 | 69 | 2023 |
A novel transfer learning fault diagnosis method based on manifold embedded distribution alignment with a little labeled data K Zhao, H Jiang, Z Wu, T Lu Journal of Intelligent Manufacturing 33, 151-165, 2022 | 60 | 2022 |
A deep transfer nonnegativity-constraint sparse autoencoder for rolling bearing fault diagnosis with few labeled data X Li, H Jiang, K Zhao, R Wang IEEE access 7, 91216-91224, 2019 | 59 | 2019 |
An optimal deep sparse autoencoder with gated recurrent unit for rolling bearing fault diagnosis K Zhao, H Jiang, X Li, R Wang Measurement Science and Technology 31 (1), 015005, 2019 | 47 | 2019 |
A new data generation approach with modified Wasserstein auto-encoder for rotating machinery fault diagnosis with limited fault data K Zhao, H Jiang, C Liu, Y Wang, K Zhu Knowledge-Based Systems 238, 107892, 2022 | 35 | 2022 |
Intelligent fault diagnosis of rolling bearing using adaptive deep gated recurrent unit K Zhao, H Shao Neural Processing Letters 51, 1165-1184, 2020 | 26 | 2020 |
Class-aware adversarial multiwavelet convolutional neural network for cross-domain fault diagnosis K Zhao, Z Liu, B Zhao, H Shao IEEE Transactions on Industrial Informatics, 2023 | 24 | 2023 |
A deep ensemble dense convolutional neural network for rolling bearing fault diagnosis Z Wu, H Jiang, S Liu, K Zhao Measurement Science and Technology 32 (10), 104014, 2021 | 18 | 2021 |
Multi-source weighted source-free domain transfer method for rotating machinery fault diagnosis Q Gao, T Huang, K Zhao, H Shao, B Jin Expert Systems with Applications 237, 121585, 2024 | 14 | 2024 |
Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis K Zhao, H Jiang, X Li, R Wang International Journal of Machine Learning and Cybernetics 12, 1483-1499, 2021 | 13 | 2021 |
An efficient diagnostic strategy for intermittent faults in electronic circuit systems by enhancing and locating local features of faults Z Jia, S Wang, K Zhao, Z Li, Q Yang, Z Liu Measurement Science and Technology 35 (3), 036107, 2023 | 10 | 2023 |
Unbalanced fault diagnosis of rolling bearings using transfer adaptive boosting with squeeze-and-excitation attention convolutional neural network K Zhao, F Jia, H Shao Measurement Science and Technology 34 (4), 044006, 2023 | 10 | 2023 |
Self-paced decentralized federated transfer framework for rotating machinery fault diagnosis with multiple domains K Zhao, Z Liu, J Li, B Zhao, Z Jia, H Shao Mechanical Systems and Signal Processing 211, 111258, 2024 | 4 | 2024 |