教师队伍

副教授(副研究员)

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  • 钟建华

    性 别 :男

    出生年月:1985年2月

    系 别:机械设计系

    学 位:博士

    职 称:副教授

  • 详细资料

    联系方式

    通讯地址:福建省福州市福州地区大学新区学园路2号 邮编:350116

    电子邮箱:jhzhong@fzu.edu.cn

    电 话:0591-22866790

    教育工作经历

    2017/02-至今,开云全站中国有限公司,开云全站中国有限公司,讲师

    2017/09-2018/02,台湾国立海洋大学,机械与机电工程系,访问学者

    2016/07,澳门大学,机电工程系,博士

    2011/07,澳门大学,机电工程系,硕士

    2008/07,开云全站中国有限公司,机械设计制造及其自动化,本科

    社会和学术兼职

    1、IEEE Member; 福建省工程图学学会副秘书长;

    2、国际期刊审稿人:

    IEEE Transactions on Industrial Electronics;

    Mechanical Systems and Signal Processing;

    Journal of Sound and Vibration;

    Neurocomputing;

    IEEE Access;

    Sensors.

    研究方向

    1、旋转设备信号处理;

    2、设状态监测与故障诊断;

    3、模式识别;

    4、机器学习算法.

    主要科研项目

    1. 福建省自然科学基金项目(面上),2019J01211,基于多重概率机器算法的风电齿轮箱耦合故障诊断研究,2019/04-2022/3,在研,主持;

    2. 开云全站中国有限公司人才引进项目,GXRC-17029, 基于专家会诊的旋转设备故障诊断,2017/06-2019/06,在研,主持;

    3. 福建省教育厅中青年教师教育科研项目,JAT170090,基于多重概率分类器的风力发电机齿轮箱故障诊断,2017/7-2019/3,已结题,主持;

    4. 科技部与澳门联合资助项目, MoST-FDCT (015/2015/AMJ),Intelligent Monitoring, Reliability Evaluation and Power Generation Anticipation of Wind Turbine(风力涡轮机的智能监测、可靠性评估和发电预期研究),03/2016-02/2019,已结题,主要参与成员;

    5. 澳门大学研究基金项目,MYRG2015-00077-FST,Sparse Bayesian Extreme Learning Committee Machine for Engine Simultaneous Fault Diagnosis(基于稀疏贝叶斯极限学习机算法的引擎耦合故障诊断研究),04/2015-03/2018,已结题,主要参与成员;

    6. 澳门大学研究基金项目,MYRG153(Y1-L2)-FST11-YZX,“Feature Extraction and Support Vector Machines Method for Fault Diagnosis of Power Generation Equipment(基于特征提取与支持向量机算法的发电设备故障诊断研究),06/2011-05/2013,已结题,主要参与成员;

    7. 澳门大学研究基金项目,078/09-10S/YZX/FST,Condition Monitoring based Systematic Modeling of Equipment(基于系统模型的设备状态监测),2010/01-2011/12,已结题,主要参与成员。

    代表性论著

    期刊论文

    1. Liang J., Zhang Y.,Zhong J. H*. and Yang H. T.,A novel multi-segment feature fusion based fault diagnosis approach for rotating machinery,Mechanical Systems and Signal Processing , 2019,122:19-41;

    2. Zhong J. H.,Zhang J., Liang J. andWang H. Q.,Multi-fault rapid diagnosis for wind turbine gearbox using sparse Bayesian extreme learning machine, IEEE Access, 2019,7:773-781;

    3. Zhong J. H.*,Wong P. K. and Yang Z. X., Fault diagnosis of rotating machinery based on multiple probabilistic classifiers,Mechanical Systems and Signal Processing, 2018,108:99-114;

    4. Ma X. B., Wong P. K.,Zhao J., Zhong J. H.*,Huang Y. and Xu X.,Design and Testing of a Nonlinear Model Predictive Controller for Ride Height Control of Automotive Semi-active Air Suspension Systems,IEEE Access, 2018,6:63777-63793;

    5. Zhong J. H., Wong P. K. and Yang Z. X.*, Simultaneous-fault diagnosis of gearboxes using probabilistic committee machine,Sensors, 2016,16(2):185;

    6. Zhong J. H., Yang Z. X.* and Wong P. K., An effective fault feature extraction method for gas turbine generator system diagnosis, Shock and Vibration, 2016, 2016:1-9;

    7. Liang J.,Zhong J. H.* and Yang Z. X.,Correlated EEMD and effective feature extraction for both periodic and irregular faults diagnosis in rotating machinery,Energies,2017, 10(10):1652;

    8. Yang Z. X. andZhong J. H.*, A Hybrid EEMD-based SampEn and SVD for Acoustic Signal Processing and Fault Diagnosis,Entropy, 2016, 18(4):112;

    9. Wong P. K.,Zhong J. H, Yang Z. X.* and Vong C. M., A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine,Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, 2017, 213(6):1146-1161;

    10. Wong P. K.*,Zhong J. H.,Yang Z. X. and Vong C. M., Sparse Bayesian Extreme Learning Committee Machine for Engine Simultaneous Fault Diagnosis, Neurocomputing, 2016, 174:331-343;

    11. Yang Z. X.*, Wang X. andZhong J. H.,Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach,Energies, 2016, 9(6):379;

    12. Wong P. K.*,Yang Z. X., Vong C. M. andZhong J. H., Real-Time Fault Diagnosis for Gas Turbine Generator Systems using Extreme Learning Machine, Neurocomputing, 2014. 128: 249-257;

    13. Yang Z. X.*, Wong P. K., Vong C. M.,Zhong J. H. and Liang J., Simultaneous-Fault Diagnosis of Gas Turbine Generator Systems Using a Pairwise-Coupled Probabilistic Classifier, Mathematical Problems in Engineering, 2013,2013(3):723-740.

    会议论文

    1. Yang Zhixin, Hoi Wui Ian andZhong J. H., Gearbox Fault Diagnosis based on Artificial Neural Network and Genetic Algorithms,Proceedings of International Conference on System and Engineering, pp. 37-42, 2011.;

    2. Yang Zhixin,Zhong J. H. and Wong Seng Fat, Machine Learning Method with Compensation Distance Technique for Gear Fault Detection,Proceedings of World Congress on Intelligent Control and Automation, pp. 632-637, 2011;

    3. Zhong J. H., Yang Zhixin and Wong Seng Fat, Machine Condition Monitoring and Fault Diagnosis based on Support Vector Machine,Proceedings of International Conference on Industrial Engineering and Engineering Management, pp.2228-2233, 2010.