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张孝远

作者: 发布于:2024-03-01 15:59:09 点击量:打印此页

    一、个人简介

    张孝远,男,1981年4月生,工学博士、教授、硕士生导师,公海清洁能源智慧运维团队负责人。兼任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12月到公海公海7108线路任教,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。主持参与国家级、省部级等纵向项目15项;主持参与水力发电公司、中国电科院,省电力公司等重点企业委托横向项目11项。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,一作Top期刊论文7篇,ESI高被引论文3篇。截至2023.6.13日,论文合计他引1340次。成果支撑获得2017年教育部自然科学奖一等奖、2022年湖北省科技进步一等奖。

    ResearchGate主页:https://www.researchgate.net/profile/Xiaoyuan_Zhang2

    联系方式:Email:shawin@foxmail.com; QQ/WeChat:381102027

    二、研究方向

    (1)发电设备状态监测、健康维护、故障诊断与健康管理;

    (2)水力、风力与光伏发电系统的建模仿真与优化控制;

    (3)新能源电力系统调度、微网的能量管理与控制;

    (4)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, Xianbo Wang, Chaoshun Li,Health Condition Assessment for Pumped Storage Units using Multi-Head Self-Attentive Mechanism and Improved Radar Chart,IEEE Transactions on Industrial Informatics. 2022.中科院1区,top期刊,IF:11.648

    [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. 中科院1区,top期刊, IF:8.934

    [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. (中科院二区,IF:5.131,ESI高被引论文)

    [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. (中科院一区,Top, IF: 8.934, ESI高被引论文)

    [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. (中科院二区,IF:5.131)

    [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. (中科院二区,Top,IF:5.779)

    [7] 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. (中科院一区,Top, IF: 4.397)

    [8] 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. (中科院一区,Top, IF: 8.665)

    [9] Xianbo Wang, Xiaoyuan Zhang, Zhen Li, Jun Wu, Ensemble Extreme Learning Machines for Compound fault Diagnosis of Rotating Machinery, Knowledge-Based Systems, 2020, 188: 105012. (中科院一区,Top, IF: 8.139)

    [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, 2022,Early Access, DOI: 10.1109/ TMECH. 2022. 3214505. (中科院一区,Top, IF: 5.867)

    [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. (中科院二区,IF: 7.632)

    [12] 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,网络首发,2022.5(EI)

    [13] 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. (EI)

    [14] 张孝远,周建中,王常青. 考虑样本交叠的水电机组振动故障诊断,电力系统保护与控制,2012, 40(3): 8-14. (EI)

    [15] 张孝远,周建中,黄志伟. 基于粗糙集和多类支持向量机的水电机组振动故障诊断. 中国电机工程学报, 2010, 30(20): 88-93. (EI)

    (二)获奖

    [1] 成果“大型水电机组动力学建模、故障诊断与优化控制”获2017年教育部自然科学一等奖,排名5。


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