邓晓刚，副教授，硕士生导师

自动化系

通信地址：青岛市黄岛区长江西路66号信息与控制工程学院， 邮编：266580

联系电话：0532-86983472  

Email: dengxiaogang@upc.edu.cn

教育背景

²  2002—2008 kaiyun体育登录网页入口(华东) 信息与控制工程学院获工学博士学位

²  1998—2002 石油大学(华东) 自动化系获学士学位

工作背景

²  2011.1-至今, kaiyun体育登录网页入口（华东）, 信息与控制工程学院, 副教授

²  2008.1-2010.12, kaiyun体育登录网页入口（华东）, 信息与控制工程学院, 讲师

²  2015.11-2016.10, 英国南安普顿大学, 电子与计算机科学系, 访问学者

学术兼职

²  IEEE会员

²  中国自动化学会技术过程故障诊断与安全性专业委员会委员

²  担任IEEE Transactions on Neural Networks and Learning Systems，Industrial & Engineering Chemistry Research, IEEE Transactions on Control System Technology 等多个国际期刊的学术审稿人

研究方向

[1]  工业过程监控与故障诊断技术

[2]  工业过程质量监控技术

[3]   控制系统性能评价技术

[4]   机器学习方法在工业数据分析中的应用

公司产品

已经指导研究生毕业7人，在读研究生10余人。

研究项目

²  国家自然科学基金：基于局部信息熵的多牌号聚丙烯过程故障诊断方法研究，2015-2017，负责人。

²  山东省自然科学基金：基于局部子空间模型的聚丙烯牌号切换过程故障诊断方法研究，2014-2017，负责人。

²  山东省重点研发计划：基于深度核学习理论的抽油机井故障诊断技术研究，2018-2019，负责人。

²  浙江大学工业控制技术国家重点实验室开放课题：基于深度核学习理论的石油化工过程微小故障检测方法研究，2019.4-2019.12，负责人。

²  kaiyun体育登录网页入口自主创新科研计划：基于稀疏核建模技术的非线性统计过程监控方法研究，2010-2012，负责人。

学术成果与获奖

[1]  Deng Xiaogang, Tian Xuemin, Chen Sheng, Harris C J. Nonlinear process fault diagnosis based on serial principal component analysis. IEEE Transactions on Neural Networks & Learning Systems, 2018, 29(3): 560-572. （SCI一区期刊）

[2]  Deng Xiaogang, Tian Xuemin, Chen Sheng, Harris C J. Deep principal component analysis based on layerwise feature extraction and its application to nonlinear process monitoring. IEEE Transactions on Control System Technology, 2018, pp(99): 1-15.（SCI二区期刊）

[3]  Deng Xiaogang, Deng Jiawei. Incipient fault detection for chemical processes using two-dimensional weighted SLKPCA. Industrial & Engineering Chemistry Research, 2019, 58(6): 2280-2295.（SCI二区期刊）

[4]  Deng Xiaogang, Wang Lei. Modified kernel principal component analysis using double-weighted local outlier factor and its application to nonlinear process monitoring. ISA Transactions, 2018, 72: 218-228 （SCI二区期刊）

[5]  Xu Ying, Deng Xiaogang. Fault detection of multimode non-Gaussian dynamic process using dynamic Bayesian independent component analysis. Neurocomputing, 2016, 200: 70-79. （SCI二区期刊）

[6]  Zhang Hanyuan, Tian Xuemin, Deng Xiaogang, Cao Yuping. Multiphase batch process with transitions monitoring based on global preserving statistics slow feature analysis. NEUROCOMPUTING, 2018, 293: 64-86  （SCI二区期刊）

[7]  Zhang Hanyuan, Tian Xuemin, Deng Xiaogang, Cao Yuping. Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis. ISA Transactions, 2018, 79: 108-126 （SCI二区期刊）

[8]  Deng Xiaogang，Zhong Na，Wang Lei. Nonlinear multimode industrial process fault detection using modified kernel principal component analysis. IEEE Access, 2017, 5: 23121-23132. （SCI二区期刊）

[9]  Cao Yuping, Hu Yongping, Deng Xiaogang, Tian Xuemin. Quality-relevant batch process fault detection using a multiway multi-subspace CVA method. IEEE Access, 2017, 5: 23256-23265 （SCI二区期刊）

[10]  Deng Xiaogang, Tian Xuemin, Chen Sheng, Harris C J. Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes. Chemometrics and Intelligent Laboratory Systems, 2017, 162: 21-34 （SCI三区期刊）

[11] Zhong Na, Deng Xiaogang. Multimode non‐Gaussian process monitoring based on local entropy independent component analysis. The Canadian Journal of Chemical Engineering, 2017, 95(2): 319-330  （SCI四区期刊）

[12] Wang Lei, Deng Xiaogang, Cao Yuping. Multimode complex process monitoring using double-level local information based local outlier factor method. Journal of Chemometrics, 2018, 32(10)： 1-21 （SCI四区期刊）

[13] Deng Xiaogang, Tian Xuemin. Entropy principal component analysis and its application to nonlinear chemical process fault diagnosis. Asian-Pacific Journal of Chemical Engineering, 2014, 9(5): 696–706  （SCI四区期刊）

[14] Deng Xiaogang, Tian Xuemin. Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis. Chinese Journal of Chemical Engineering,2014, 22(11-12): 1260-1267 （SCI四区期刊）

[15] Zhang Ni, Tian Xuemin, Cai Lianfang, Deng Xiaogang. Process fault detection based on dynamic kernel slow feature analysis. Computers & Electrical Engineering, 2015, 41:9-17 （SCI四区期刊）

[16] Zhang Hanyuan, Tian Xuemin, Deng Xiaogang, Cai Lianfang. A local and global statistics pattern analysis method and its application to process identification. Chinese Journal of Chemical Engineering, 2015, 23(11): 1782-1792 （SCI四区期刊）

[17] Deng Xiaogang, Tian Xuemin. Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection. Chinese Journal of Chemical Engineering, 2013, 21(2), 163-170 （SCI四区期刊）

[18] Deng Xiaogang, Tian Xuemin, Chen Sheng. Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis. Chemometrics and Intelligent Laboratory Systems, 127(2013):195-209  （SCI二区期刊）

[19]  Deng Xiaogang, Tian Xuemin. Nonlinear process fault pattern recognition using statistics kernel PCA similarity factor. Neurocomputing, 2013, 121：298-308 （SCI三区期刊）

[20] 邓晓刚，邓佳伟，曹玉苹，王磊。基于双层局部KPCA的非线性过程微小故障检测方法。化工学报，2018， 69（7）：3092-3100

[21] 马建，邓晓刚，王磊. 基于深度集成支持向量机的工业过程软测量方法. 化工学报，2018， 69（3）：1121-1128

[22] 王磊，邓晓刚，徐莹，钟娜. 基于变量子域PCA 的故障检测方法. 化工学报，2016，67（10）：4300-4307

[23] 徐莹，邓晓刚，钟娜. 基于ICA混合模型的多工况过程故障诊断方法. 化工学报，2016,67（9）：3793-3803 （国内EI期刊）

[24] 钟娜, 邓晓刚, 徐莹. 基于LECA的多工况过程故障检测方法[J]. 化工学报, 2015, 66(12): 4929-4940.

[25] 王磊，邓晓刚，曹玉苹，田学民。基于MLFDA的化工过程故障模式分类方法。山东大学学报（工学版），2017，47（5）：179-186

[26] 张琛琛, 邓晓刚, 徐莹. 基于改进MKECA的间歇过程故障检测方法研究[J]. 控制工程, 2018，25(4): 636-642

[27] Deng Xiaogang, Tian Xuemin. Multiple Component Analysis and its application in Process monitoring with Prior Fault data. The 9th IFAC Symposium on Fault detection, Supervision and Safety of Technical Processes, Sep.2-4, 2015, Paris, France.

[28] Deng Xiaogang, Tian Xuemin, Chen Sheng, Harris C. J.. Statistics local Fisher discriminant analysis for industrial process fault classification. UKACC CONTROL 2016, Belfast, Northern Ireland, UK. 31.Aug-2. Sep., 2016.

[29] Zhong Na, Deng Xiaogang. Local entropy principal component analysis and its application for multimode process monitoring. 2016 12^th World Congress on Intelligent Control and Automation (WCICA), June 12-15, Guilin China, pp. 1291-1296.

[30] Deng Xiaogang, Sun Baowei, Wang Lei. Improved kernel Fisher discriminant analysis for nonlinear process fault pattern recognition. Proceedings of 2018 IEEE 7^th Data Driven Control and Learning Systems Conference, DDCLS 2018, Enshi, Hubei China, May 25-27, 2018.