个人简介
教育背景
2002—2008 中国石油大学(华东) 信息与控制工程学院获工学博士学位
1998—2002 石油大学(华东) 自动化系获学士学位
工作背景
2011.1-至今, 中国石油大学（华东）, 信息与控制工程学院, 副教授
2008.1-2010.12, 中国石油大学（华东）, 信息与控制工程学院, 讲师
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
研究领域
工业过程监控与故障诊断技术
工业过程质量监控技术
控制系统性能评价技术
机器学习方法在工业数据分析中的应用
近期论文
查看导师新发文章
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[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）
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