Xiaoyuan Zhang
1. Personal Profile
Xiaoyuan Zhang, male, holds a Ph.D. in engineering, is a professor, and a doctoral supervisor. He is the leader of the Clean Energy Smart Operation and Maintenance Team at Henan University of Technology. He also serves as the Deputy Secretary-General of the Smart Hydropower and Equipment Committee of the Yangtze River Technical Economics Society, and as a member of the Rotor Dynamics Committee of the China Vibration Engineering Society, the Henan Electrical Engineering Society, and the Henan Instrument and Instrumentation Society. Zhang received his Ph.D. from the School of Hydropower and Digital Engineering at Huazhong University of Science and Technology in 2012. In the same year, he joined the School of Electrical Engineering at Henan University of Technology, where he was promoted to associate professor in November 2015. He was a visiting scholar at Purdue University and Indiana University from July 2019 to July 2020. He was promoted to full professor in January 2024.
He has led 15 vertical and horizontal projects, including 2 National Natural Science Foundation projects, 1 Henan Provincial Natural Science Foundation general project, and commissioned projects from large state-owned enterprises. His research achievements have contributed to winning the First Prize of the Ministry of Education Natural Science Award in 2017 (5th place) and the First Prize of the Hubei Provincial Science and Technology Progress Award in 2022 (8th place). He has been honored with titles such as Excellent Master's Thesis Supervisor in Henan Province and Outstanding Graduate Supervisor in the 5th session at Henan University of Technology. He has published 33 papers in authoritative journals, including 17 SCI papers, 3 ESI highly cited papers, with over 2000 citations on Google Scholar.
ResearchGate profile:
https://www.researchgate.net/profile/Xiaoyuan_Zhang2
Email: freedon@haut.edu.cn; QQ/WeChat: 381102027
2. Research Focus
· (1) Power Generation Equipment Condition Monitoring, Fault Diagnosis, and Condition-Based Maintenance
· (2) "Water-Wind-Solar-Storage" System Optimization
· (3) Big Data, Deep Learning, and Artificial Intelligence Applications
3. Main publications
[1] Xiaoyuan Zhang*, Yajun Jiang, Xian-bo Wang, Chaoshun Li, Jinhao Zhang, Health Condition Assessment for Pumped Storage Units Using Multihead Self-Attentive Mechanism and Improved Radar Chart. IEEE Transactions on Industrial Informatics, 2022, 18(11): 8087-8097.
[2] Xiaoyuan Zhang*, Yajun Jiang, Chaoshun Li, Jinhao Zhang, Health status assessment and prediction for pumped storage units using a novel health degradation index. Mechanical Systems and Signal Processing, 2022, 171: 108910.
[3] Xiaoyuan Zhang*, Chaoshun Li, Xianbo Wang, Huanmei Wu. A novel fault diagnosis procedure based on improved symplectic geometry mode decomposition and optimized SVM. Measurement, 2021,173: 108644.
[4] Xiaoyuan Zhang*, Mengnan Liu, Yingying Liu. Battery state of health estimation using an AutoGluon-tabular model incorporating uncertainty quantification, Journal of Energy Storage, 2024,101:113920.
[5] Xiaoyuan Zhang*, Ziqiang Zhao, Rui Shao, Chaoshun Li, Huizeng Tang. Mechanical Anomaly Detection and Early Warning for Ultra-high Voltage Shunt Reactors via Adaptive Thresholds and WGAN-GP, IEEE Sensors journal. 2024, 24(12):1558-1748.
[6] Xiaoyuan Zhang*, Yitao Liang, Jianzhong Zhou, A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM, Measurement. 2015, 69： 164–179.
[7] Xiaoyuan Zhang*, Daoyin Qiu, Fuan Chen, Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis, Neurocomputing. 2015, 149: 641–651.
[8] Xiaoyuan Zhang*, Jianzhong Zhou, Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mechanical Systems and Signal Processing, 2013, 41(1): 127-140.
[9] Xiaoyuan Zhang*, Jianzhong Zhou, Chaoshun Li. Multi-class support vector machine optimized by inter-cluster distance and self-adaptive differential evolution. Applied Mathematics and Computation， 2012, 9 (1): 4973-4987.
[10] Xiaoyuan Zhang*, Jianzhong Zhou, Jun Guo. Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine. Expert Systems with Applications, 2012, 39 (3): 2621-2628.
[11] Meng Luo, Chaoshun Li, Xiaoyuan Zhang, Ruhai Li, Xueli An, Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings, ISA Transaction. 2016, 65: 556–566.
[12] Xian-bo Wang, Xiaoyuan Zhang, Zhen Li, Jun Wu, Ensemble extreme learning machines for compound-fault diagnosis of rotating machinery, Knowledge-Based Systems, 2020, 188: 105012.
[13] Cui Xiaolong, Yifan Wu, Xiaoyuan Zhang, Jie Huang, Pak Kin Wong, Chaoshun Li, A Novel Fault Diagnosis Method for Rotor-Bearing System Based on Instantaneous Orbit Fusion Feature Image and Deep Convolutional Neural Network, IEEE/ASME Transactions on Mechatronics, 2023, 28(2): 1013-1024.
[14] Peng Chen, Chaoshun Li, Xiaoyuan Zhang, Degradation trend prediction of pumped storage unit based on a novel performance degradation index and GRU-attention model, Sustainable Energy Technologies and Assessments, 2022, 54: 102807.
[15] Tian Yu, Chaoshun Li, Jie Huang, Xiangqu Xiao, Xiaoyuan Zhang, ReF-DDPM: A novel DDPM-based data augmentation method for imbalanced rolling bearing fault diagnosis, Reliability Engineering & System Safety, 2024,251(110343).
