一、个人简介
张孝远，男，1981年4月生，工学博士、教授、博士生导师，河南工业大学清洁能源智慧运维团队负责人。兼任长江技术经济学会智慧水电与装备专委会副秘书长、中国振动工程学会转子动力学专委会、河南省电工技术学会、河南省仪器仪表学会理事。2012年博士毕业于华中科技大学水电与数字化工程学院，同年到河南工业大学电气工程学院任教，2015年11月取得副教授职称；2019.7-2020.7月，美国普渡大学印第安纳大学联合分校访问学者；2024年1月取得教授职称。
主持国家自然科学基金（2项）、河南省自然科学基金面上项目（1项）等纵向课题及大型国有企业委托横向课题15项。研究成果支撑获得2017年教育部自然科学一等奖（排序5）、2022年湖北省科技进步一等奖（排序8）。获河南省优秀硕士学位论文指导教师、河南工业大学第五届研究生优秀指导教师等荣誉称号。在IEEE Transactions on Industrial Informatics、Mechanical Systems and Signal Processing、Neurocomputing、中国电机工程学报等国内外权威期刊发表论文27篇，包含SCI期刊论文17篇，ESI高被引论文3篇，Google学术引用1500余次。
ResearchGate主页：
https://www.researchgate.net/profile/Xiaoyuan_Zhang2
Email：freedon@haut.edu.cn; QQ/WeChat：381102027
二、研究方向
（1）发电设备状态监测、故障诊断与状态检修；
（2）“水风光储”系统优化运行；
（3）大数据、深度学习和人工智能的应用研究。
三、代表性成果
（一）主要论文
[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, 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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. May.6, 2024, early access, doi:10.1109/JSEN.2024.3395437.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[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] 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.
[14]张孝远,张金浩,杨立新.考虑不同充电策略的锂电池健康状态区间估计，上海交通大学学报, 2024, 58(3): 273-284.
[15]张孝远,张新萍，苏宝平，基于最小最大核K均值聚类算法的水电机组振动故障诊断，电力系统保护与控制，2015，43 (5): 27-34.
[16]张新萍，张孝远,基于差分进化算法的模糊核聚类算法及其在故障诊断中的应用,电力系统保护与控制，2014, 42 (17): 102-106.
[17]张孝远，张金浩，蒋亚俊，基于改进TCN模型的动力电池健康状态评估，储能科学与技术，储能科学与技术. 2022,11(05): 1617-1626.
（二）获奖
1.特大型水电机组智能运维关键技术、成套装备及产业化，湖北省科学技术进步奖，一等奖，湖北省政府，2022，（8/15）
2.大型水电机组动力学建模、故障诊断与优化控制，高等学校科学研究优秀成果奖（自然科学），一等奖，教育部，2017,(5/5)
