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，学院，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，学院，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. 学院人才引进项目，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.