| A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM X Zhang, Y Liang, J Zhou Measurement 69, 164-179, 2015 | 585 | 2015 |
| Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines X Zhang, J Zhou Mechanical Systems and Signal Processing 41 (1-2), 127-140, 2013 | 299 | 2013 |
| Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings M Luo, C Li, X Zhang, R Li, X An Isa Transactions 65, 556-566, 2016 | 113 | 2016 |
| Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis X Zhang, D Qiu, F Chen Neurocomputing 149, 641-651, 2015 | 107 | 2015 |
| Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine X Zhang, J Zhou, J Guo, Q Zou, Z Huang Expert Systems with Applications 39 (3), 2621-2628, 2012 | 78 | 2012 |
| Ensemble extreme learning machines for compound-fault diagnosis of rotating machinery XB Wang, X Zhang, Z Li, J Wu Knowledge-Based Systems 188, 105012, 2020 | 75 | 2020 |
| A novel fault diagnosis procedure based on improved symplectic geometry mode decomposition and optimized SVM X Zhang, C Li, X Wang, H Wu Measurement 173, 108644, 2021 | 70 | 2021 |
| Blind Parameter Identification of MAR Model and Mutation Hybrid GWO‐SCA Optimized SVM for Fault Diagnosis of Rotating Machinery W Fu, J Tan, X Zhang, T Chen, K Wang Complexity 2019 (1), 3264969, 2019 | 60 | 2019 |
| Multi-class support vector machine optimized by inter-cluster distance and self-adaptive deferential evolution X Zhang, J Zhou, C Wang, C Li, L Song Applied Mathematics and Computation 218 (9), 4973-4987, 2012 | 35 | 2012 |
| Ensuring profitability of retailers via Shapley Value based demand response J Wang, Q Huang, W Hu, J Li, Z Zhang, D Cai, X Zhang, N Liu International Journal of Electrical Power & Energy Systems 108, 72-85, 2019 | 29 | 2019 |
| Health status assessment and prediction for pumped storage units using a novel health degradation index X Zhang, Y Jiang, C Li, J Zhang Mechanical Systems and Signal Processing 171, 108910, 2022 | 18 | 2022 |
| 基于粗糙集和多类支持向量机的水电机组振动故障诊断 张孝远， 周建中， 黄志伟， 李超顺， 贺徽 中国电机工程学报, 88-93, 2010 | 18 | 2010 |
| Health condition assessment for pumped storage units using multihead self-attentive mechanism and improved radar chart X Zhang, Y Jiang, XB Wang, C Li, J Zhang IEEE Transactions on Industrial Informatics 18 (11), 8087-8097, 2022 | 13 | 2022 |
| Vibrant fault diagnosis for hydro-turbine generating unit based on rough sets and multi-class support vector machine XY Zhang, JZ Zhou, ZW Huang, CS Li, H He Zhongguo Dianji Gongcheng Xuebao(Proceedings of the Chinese Society of …, 2010 | 12 | 2010 |
| Support vector machine with parameter optimization by bare bones differential evolution D Qiu, Y Li, X Zhang, B Gu 2011 Seventh International Conference on Natural Computation 1, 263-266, 2011 | 11 | 2011 |
| 水轮发电机组轴系松动-碰摩耦合故障的动态响应 黄志伟， 周建中， 张孝远， 王常青， 张勇传 西南交通大学学报 46 (1), 121-126, 2011 | 9 | 2011 |
| Degradation trend prediction of pumped storage unit based on a novel performance degradation index and GRU-attention model P Chen, C Li, X Zhang Sustainable Energy Technologies and Assessments 54, 102807, 2022 | 7 | 2022 |
| A novel fault diagnosis method for rotor-bearing system based on instantaneous orbit fusion feature image and deep convolutional neural network X Cui, Y Wu, X Zhang, J Huang, PK Wong, C Li IEEE/ASME Transactions on Mechatronics 28 (2), 1013-1024, 2022 | 7 | 2022 |
| 基于 Levy-ABC 优化 SVM 的水电机组故障诊断方法 肖剑， 周建中， 张孝远， 李超顺， 寇攀高， 肖汉 振动. 测试与诊断 33 (5), 839-844, 2013 | 7 | 2013 |
| 基于滑模变结构控制的水轮机调节系统 寇攀高， 周建中， 张孝远， 王常青 电网技术 36 (8), 157-162, 2012 | 7 | 2012 |
Chaoshun LiHuazhong University of Science and Technology在 hust.edu.cn 的电子邮件经过验证
