| SuperGraph: Spatial-temporal graph-based feature extraction for rotating machinery diagnosis C Yang, K Zhou, J Liu IEEE Transactions on Industrial Electronics 69 (4), 4167-4176, 2021 | 142 | 2021 |
| Dynamic graph-based feature learning with few edges considering noisy samples for rotating machinery fault diagnosis K Zhou, C Yang, J Liu, Q Xu IEEE Transactions on Industrial Electronics 69 (10), 10595-10604, 2021 | 79 | 2021 |
| An improved multi-channel graph convolutional network and its applications for rotating machinery diagnosis C Yang, J Liu, K Zhou, X Jiang, X Zeng Measurement 190, 110720, 2022 | 48 | 2022 |
| Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning J Liu, K Zhou, C Yang, G Lu Frontiers of Mechanical Engineering 16 (4), 829-839, 2021 | 41 | 2021 |
| Deep graph feature learning-based diagnosis approach for rotating machinery using multi-sensor data K Zhou, C Yang, J Liu, Q Xu Journal of Intelligent Manufacturing 34 (4), 1965-1974, 2023 | 39 | 2023 |
| A node-level PathGraph-based bearing remaining useful life prediction method C Yang, J Liu, K Zhou, X Jiang, MF Ge, Y Liu IEEE Transactions on Instrumentation and Measurement 71, 1-10, 2022 | 34 | 2022 |
| Transferable graph features-driven cross-domain rotating machinery fault diagnosis C Yang, J Liu, K Zhou, MF Ge, X Jiang Knowledge-Based Systems 250, 109069, 2022 | 33 | 2022 |
| A generalized graph contrastive learning framework for few-shot machine fault diagnosis C Yang, J Liu, Q Xu, K Zhou IEEE Transactions on Industrial Informatics 20 (2), 2692-2701, 2023 | 28 | 2023 |
| Semisupervised machine fault diagnosis fusing unsupervised graph contrastive learning C Yang, J Liu, K Zhou, X Jiang IEEE Transactions on Industrial Informatics 19 (8), 8644-8653, 2022 | 22 | 2022 |
| Transfer graph-driven rotating machinery diagnosis considering cross-domain relationship construction C Yang, J Liu, K Zhou, X Yuan, MF Ge IEEE/ASME Transactions on Mechatronics 27 (6), 5351-5360, 2022 | 20 | 2022 |
| A meta-path graph-based graph homogenization framework for machine fault diagnosis C Yang, J Liu, K Zhou, X Yuan, X Jiang Engineering Applications of Artificial Intelligence 121, 105960, 2023 | 13 | 2023 |
| Remaining useful life prediction for rotating machinery based on dynamic graph and spatial–temporal network X Zeng, C Yang, J Liu, K Zhou, D Li, S Wei, Y Liu Measurement Science and Technology 34 (3), 035102, 2022 | 13 | 2022 |
| Dynamic spatial–temporal graph-driven machine remaining useful life prediction method using graph data augmentation C Yang, J Liu, K Zhou, X Li Journal of Intelligent Manufacturing 35 (1), 355-366, 2024 | 10 | 2024 |
| Channel attention-based static-dynamic graph convolutional network for lithology identification with scarce labels Z Geng, J Liu, S Li, C Yang, J Zhang, K Zhou, J Tang Geoenergy Science and Engineering 223, 211526, 2023 | 10 | 2023 |
| Source-free cluster adaptation for privacy-preserving machinery fault diagnosis M Zhu, X Zeng, J Liu, C Yang, K Zhou IEEE Transactions on Instrumentation and Measurement 72, 1-10, 2023 | 6 | 2023 |
| An improved KNNG driven graph transfer diagnosis method via edge predictor C Yang, Y Hu, S Yang, J Liu 2023 5th International Conference on Intelligent Control, Measurement and …, 2023 | 3 | 2023 |
| Dynamic Graph-Driven Rotating Machine Fault Diagnosis: An Adaptively Updating Cross-Domain Relationship Information C Yang, J Liu, Y Hu, B Wu, T Shi IEEE Transactions on Industrial Informatics, 2024 | 1 | 2024 |
| Power spectral entropy-based graph construction for rotating machinery diagnosis using multi-sensor C Yang, K Zhou, J Liu, Q Xu 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing), 1-5, 2021 | 1 | 2021 |
| An Improved Graph Sampling-Based Machine Fault Diagnosis Under Edge Computing Architecture J Chen, J Liu, C Yang, S Liu, X Qin, Z Fang 2023 International Conference on Sensing, Measurement & Data Analytics in …, 2023 | | 2023 |
| Unsupervised Graph Contrastive Learning-based Anomaly Detection Method for Rotating Machinery C Yang, X Zeng, J Liu, K Zhou, S Wei 2022 IEEE International Conference on Sensing, Diagnostics, Prognostics, and …, 2022 | | 2022 |