教师名录 - 钟建华
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:讲师
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,福州大学,机械工程及自动化学院,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,福州大学,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. 福州大学人才引进项目,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. and Wang 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. and Zhong 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. and Zhong 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. and Zhong 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 and Zhong 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.

 

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地址:福建省福州市福州地区大学新区学园路2号 邮编:350116