| Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks S Sun, Y Liu, Q Li, T Wang, F Chu Energy Conversion and Management 283, 116916, 2023 | 58 | 2023 |
| Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy S Sun, T Wang, H Yang, F Chu Applied Energy 313, 118882, 2022 | 39 | 2022 |
| In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures S Sun, T Wang, F Chu Renewable and Sustainable Energy Reviews 160, 112326, 2022 | 31 | 2022 |
| A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control R He, H Yang, S Sun, L Lu, H Sun, X Gao Applied Energy 326, 120013, 2022 | 29 | 2022 |
| Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function S Sun, T Wang, H Yang, F Chu Renewable Energy 181, 59-70, 2022 | 24 | 2022 |
| A generalized minimax-concave penalty based compressive beamforming method for acoustic source identification S Sun, T Wang, F Chu Journal of Sound and Vibration 500, 116017, 2021 | 16 | 2021 |
| Matching contrastive learning: an effective and intelligent method for wind turbine fault diagnosis with imbalanced SCADA data S Sun, W Hu, Y Liu, T Wang, F Chu Expert Systems with Applications 223, 119891, 2023 | 15 | 2023 |
| Uncovering correlation between train delay and train exposure to bad weather X Ling, Y Peng, S Sun, P Li, P Wang Physica A: Statistical Mechanics and its Applications 512, 1152-1159, 2018 | 15 | 2018 |
| A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data S Sun, T Wang, F Chu Renewable Energy 208, 420-430, 2023 | 13 | 2023 |
| Adversarial Representation Learning for Intelligent Condition Monitoring of Complex Machinery S Sun, T Wang, H Yang, F Chu IEEE Transactions on Industrial Electronics 70 (5), 5255-5265, 2023 | 5 | 2023 |
| Acoustic source identification using an off-grid and sparsity-based method for sound field reconstruction S Sun, T Wang, F Chu, J Tan Mechanical Systems and Signal Processing 170, 108869, 2022 | 5 | 2022 |
| An environmentally adaptive and contrastive representation learning method for condition monitoring of industrial assets S Sun, T Wang, H Yang, F Chu IEEE Transactions on Cybernetics, 2022 | 4 | 2022 |
| Damage identification of wind turbine blades using the microphone array under different parametric and measuring conditions: A prototype study with laboratory-scale models S Sun, T Wang, H Yang, F Chu Structural Health Monitoring 22 (1), 201-215, 2023 | 3 | 2023 |
| Bionic Structure Inspired by Tree Frogs to Enhance Damping Performance Y Li, H Yang, M Wiercigroch, Y Han, S Sun, D Li, Z Li, Z Li ACS Applied Materials & Interfaces 15 (26), 31979-31993, 2023 | 1 | 2023 |
| Transparent Operator Network: A Fully Interpretable Network Incorporating Learnable Wavelet Operator for Intelligent Fault Diagnosis Q Li, H Li, W Hu, S Sun, Z Qin, F Chu IEEE Transactions on Industrial Informatics, 2024 | | 2024 |
| Wind turbine blade breakage detection based on environment-adapted contrastive learning S Sun, Q Li, W Hu, Z Liang, T Wang, F Chu Renewable Energy 219, 119487, 2023 | | 2023 |
| A Fully Transparent Deep Signal Operator Network for Intelligent Fault Diagnosis Q Li, S Sun, W Hu, H Li, Z Qin, F Chu 2023 Global Reliability and Prognostics and Health Management Conference …, 2023 | | 2023 |
| A Deep Generative Digital Twin for Vehicle Steering System Health Monitoring with Limited Healthy Monitoring Samples W Hu, Q Zhou, S Sun, Q Li, H Li, T Wang, F Chu 2023 Global Reliability and Prognostics and Health Management Conference …, 2023 | | 2023 |
| Physics-Informed Scaling Evolutionary Transformer for In-Situ Tool Condition Monitoring Y Liu, T Wang, S Sun, Q Li, F Chu IEEE/ASME Transactions on Mechatronics, 2023 | | 2023 |
| Adaptive and Self-supervised Condition Monitoring Method for Machinery S Sun, T Wang, F Chu 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai), 1-7, 2022 | | 2022 |
Wang TianyangTsinghua UniversityVerified email at mail.tsinghua.edu.cn
