邓晓刚(副教授)

作者:发布者:李芳发布时间:2020-07-06浏览次数:8629

»姓名:邓晓刚

»系属:自动化系


»学位:博士

»职称:副教授

»专业:控制科学与工程

»导师类别:硕士生导师

»电子邮箱:dengxiaogang@upc.edu.cn

»联系电话:

»通讯地址:山东省青岛市黄岛区长江西路66

»概况

研究方向

基于人工智能技术的复杂系统数据挖掘与分析,具体包括:

[1]  基于机器学习与深度学习理论的工业数据解析方法

[2]  数据驱动的工业系统智能故障诊断技术

[3]  复杂非线性工业系统软测量建模技术


教育经历

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


工作经历

2011.1-至今中国石油大学(华东),自动化系副教授

2008.1-2010.12,中国石油大学(华东),自动化系讲师

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


学术兼职

IEEE会员

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

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


主讲课程

本科生课程:自动控制原理、现代控制理论、系统故障诊断技术、半导体制造与过程控制、自控课程设计,等。

       研究生课程:线性系统理论


指导研究生及博士后

20131人:徐莹 (已毕业)

20143人:钟娜、张琛琛、吴明胜(已毕业)

20153人:王磊、胡永平、孙宝伟(已毕业)

20164人:马健、高凯、路凯琪、邓佳伟(已毕业)

20175人:蔡配配、于蕾、郑雪莹、陈永炫、王欣然(已毕业)

20183人:崔文志、张政、田煜坤 (已毕业)

20194人:杜昆玉、戴佳兵、周奉玄、李森(已毕业)

20204人:江先晖、刘晓月、荆胜洁、孙瑞(已毕业)

20214人:张学鹏、赵悦、肖林勃、平植源

20224人:王自恒、黎佳妍、张静、杨文洁

20233人:王宇江、吴美聪、范中勇

 

承担项目

[1] 局部信息熵的多牌号聚丙烯过程故障诊断方法研究,国家自然科学基金青年项目,2015.01-2017.1225万,主持

[2] 深度核学习理论的抽油机井故障诊断技术研究,山东省重点研发计划项目,2018.01-2019.1225万,主持

[3] 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,山东省自然科学基金,2014.12-2017.125万,主持

[4] 基于深度核学习机(DKLM)的抽油机井故障诊断方法研究,中国石油大学自主创新项目,2017.01-2019.1215万,主持

[5] 基于液压二次调节的海洋钻井补偿绞车工作机理及预测控制算法研究,海洋物探及勘探设备国家工程实验室开放课题,2020.5-2022.48万,主持

[6] 地球物理智能信息处理与解释,中石油重大科技合作项目,2020.5.10-2022.12.31110万,参与人。

[7]先验知识辅助下数据驱动的石化过程微小故障诊断方法,山东省自然科学基金面上项目,2021.1-2023.1210万,主持。


获奖情况

[1] 2014年山东省优秀学士学位论文,指导教师。

[2] 2014年教育部西门子杯全国大学生工业自动化挑战赛运动控制赛项,总决赛一等奖,指导教师。

[3] 2015年教育部西门子杯全国大学生工业自动化挑战赛设计开发赛项,华北赛区二等奖,指导教师。

[4] 2015年第十四届山东省大学生科技文化艺术节大学生机器人大赛,一等奖,指导教师

[5] 2017年学校优秀教学成果教学实验技术类二等奖,第一完成人。

[6] 2019年学校优秀教学成果奖,二等奖,7/17

       [7] 2020年中国石油大学优秀硕士学位论文(指导教师)。

       [8] 2020年,山东省自动化学会教学成果奖,本科高校类一等奖,6/6

       [9] 2022年,山东省自动化学会科学技术奖,自然科学奖二等奖,第三完成人。

       [10] 2023年,新疆维吾尔自治区科技进步奖,二等奖,第三完成人。

       [11] 2023年,国家一流本科课程《自动控制原理》,第四参与人。


荣誉称号


著作


论文

  [1] Chen Yongxuan, Deng Xiaogang. A deep supervised learning framework based on kernel partial least squares for industrial soft sensing. IEEE Transactions on Industrial Informatics, 202319(3)3178-3187. (SCI 一区)

  [2] Deng Xiaogang, Xiao Linbo, Liu Xiaoyue, Zhang Xuepeng. One-dimensional residual GANomaly network-based deep feature extraction model for complex industrial system fault detection. IEEE Transactions on Instrument and Measurement, 2023, 72: 3520013. (SCI 二区)

  [3] Deng Xiaogang, Ping Zhiyuan, Sun rui. UWB NLOS recognition based on improved convolutional neural network assisted by wavelet analysis and Gramian angular field. IEEE Sensors Journal. 2023, 23(14), pp.16384-16392. (SCI 二区)

  [4] Zhao Yue, Deng Xiaogang, Li Sen. A nonlinear industrial soft sensor modeling method based on locality preserving stochastic configuration network with utilizing unlabeled samples. ISA Transactions, 2023, 139, pp.548-560. (SCI 二区)

  [5]  Deng Xiaogang, Zhang Xuepeng, Liu Xiaoyue, Cao Yuping. Incipient fault detection of nonlinear chemical processes based on probability-related randomized slow feature analysis. Process Safety and Environmental Protection, 2023. 169: 797-807. (SCI 二区)

  [6] Jiang Xianhui, Deng Xiaogang. Knowledge Reverse Distillation Based Confidence Calibration for Deep Neural Networks. Neural Processing Letters, 2023. 55: 345-360 (SCI 四区)

  [7] Zhang Xiangrui, Song Chunyue, Zhao Jun, Xu Zuhua, Deng Xiaogang. Deep Subdomain Learning Adaptation Network: A Sensor Fault-Tolerant Soft Sensor for Industrial Processes. IEEE Transactions on Neural Networks and Learning Systems, 2022, in press. (SCI 一区)

  [8] Zhang Xiangrui, Song Chunyue, Zhao Jun, Deng Xiaogang. Domain Adaptation Mixture of Gaussian Processes for Online Soft Sensor Modeling of Multimode Processes When Sensor Degradation Occurs. IEEE Transactions on Industrial Informatics, 2022, 18(7): 4654-4664. (SCI 一区)

  [9] Deng Xiaogang, Du Kunyu. Efficient batch process monitoring based on random nonlinear feature analysis. Canadian Journal of Chemical Engineering, 2022, 100: 1826-1837. (SCI 四区)

  [10] Deng Xiaogang, Jing Shengjie, Wang Shubin, Huang Xianri, Liu Hao. Feature-Weighted Echo State Network for Dynamic Frequency Offset Modeling of Quartz Crystal Resonators. IEEE Transactions on Ultrasonics FerroElectronics and Frequency Control, 2022, 69(11): 3211-3219 SCI二区)

  [11] Huang Lumeng, Deng Xiaogang, Bo Yingchun, Zhang Yanting, Wang Ping. Evolutionary optimization assisted delayed deep cycle reservoir modeling method with its application to ship heave motion prediction. ISA Transactions, 2022, 126: 638-648. SCI二区)

  [12] Wang Ping, Yin Yichao, Bai Wei, Deng Xiaogang, Shao Weiming. A Unified Just-in-Time Learning Paradigm and Its Application to Adaptive Soft Sensing for Nonlinear and Time-Varying Chemical Process. Chemical Engineering Science, 2022, 117753. (SCI 二区)

  [13] Wang Ping, Yin Yichao, Deng Xiaogang, Bo Yingchun, Shao Weiming. Semi-supervised echo state network with temporal-spatial graph regularization for dynamic soft sensor modeling of industrial processes. ISA Transactions, 2022, 130: 306-315. (SCI 二区)

  [14] Deng Xiaogang, Wang Shubin, Jing Shengjie, Huang Xianri, Huang Weixing, Cui Baochun. Dynamic frequency-temperature characteristic modeling for quartz crystal resonator based on improved echo state network. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2022, 69(1): 438-446. (SCI 二区)

  [15] Deng Xiaogang, Liu Xiaoyue, Cao Yuping, Cong Lin, Li Zhe. Incipient fault detection for dynamic chemical processes based on enhanced CVDA integrated with probability information and fault-sensitive features. Journal of Process Control, 2022, 114, 29-41. SCI二区)

  [16] Zhang Xiangrui, Deng Xiaogang, Wang Ping. Double-level locally weighted extreme learning machine for soft sensor modeling of complex nonlinear industrial processes. IEEE Sensors Journal. 2021, 21: 1897-1905. SCI二区)

  [17] Zhang Zheng, Deng Xiaogang. Anomaly detection using improved deep SVDD model with data structure preservation. Pattern Recognition letters, 2021, 148: 1-6.  SCI三区)

  [18] Deng Xiaogang, Wang Shubing, Huang Xianri, Liu Hao, Cui Baochun. Modified Modeling Method of Quartz Crystal Resonator Frequency-Temperature Characteristic With Considering Thermal Hysteresis. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2021, 68(3): 890-898. SCI二区)

  [19] Cai Peipei, Deng Xiaogang. Incipient fault detection for nonlinear processes based on dynamic multi-block probability related kernel principal component analysis. ISA Transactions. 2020 105210-220. SCI二区)

  [20] Deng Xiaogang, Cai Peipei, Cao Yuping, Wang Ping. Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes. Industrial & Engineering Chemistry Research, 2020, 59 (13), 5956-5968. SCI三区)

  [21] 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, 2019, 27(6): 2526-2540. SCI二区)

  [22] 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三区)

  [23] 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)

  [24] 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 Systems Technology, 2019, 27(6): 2526-2540. SCI二区)

  [25] 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)

  [26] Deng Xiaogang, Cai Peipei, Cao Yuping, Wang Ping. Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes. Industrial & Engineering Chemistry Research, 2019, 59 (13), 5956-5968. SCI二区)

  [27] 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)

  [28] 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)

  [29] 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)

  [30] 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)

  [31] Wang Lei, Deng Xiaogang. Multiblock Principal Component Analysis Based on Variable Weight Information and Its Application to Multivariate Process Monitoring. Canadian Journal of Chemical Engineering, 2018, 96: 1127-1141. (SCI四区)

  [32] 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)

  [33] 王晓慧,王延江,邓晓刚,张政。基于加权深度支持向量数据描述的工业过程故障检测。化工学报,2021, 72: 5707-5716. EI收录)

  [34] 杨明辉,刘晓月,邓晓刚,廖明燕,侯春望。基于加权概率CVDA的动态化工系统微小故障检测。化工学报,2022739):3963-3972EI收录)

  [35] 邓佳伟,邓晓刚,曹玉苹,张晓玲. 基于加权统计局部核主元分析的非线性化工过程微小故障诊断方法. 化工学报,2019, 70(7):2594-2605. EI收录)

  [36] 于蕾,邓晓刚,曹玉苹,路凯琪。基于变量分组DTW-MCVA的不等长间歇过程故障检测方法。化工学报,201970(9): 3439-3446 EI收录)

  [37] 蔡配配,邓晓刚,曹玉苹,邓佳伟。基于WPRKPCA的非线性化工过程微小故障检测。化工进展,20193812):5247-5256 EI收录)

  [38] 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 697):3092-3100 EI收录)

  [39] 邓晓刚,张琛琛,王磊.基于多阶段多向核熵成分分析的间歇过程故障检测方法. 化工学报, 2017, 68(5): 1961-1968. EI收录)

  [40] 邓佳伟,邓晓刚,曹玉苹,张晓玲. 基于加权统计局部核主元分析的非线性化工过程微小故障诊断方法. 化工学报, 2019, 70(7):2594-2605. EI收录)

  [41] 于蕾,邓晓刚,曹玉苹,路凯琪。基于变量分组DTW-MCVA的不等长间歇过程故障检测方法。化工学报,201970(9): 3439-3446 EI收录)

  [42] 蔡配配,邓晓刚,曹玉苹,邓佳伟。基于WPRKPCA的非线性化工过程微小故障检测。化工进展,20193812): 5247-5256。(EI收录)

  [43] 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 693):1121-1128 EI收录)

  [44] 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,2016,679):3793-3803  EI收录)

  [45] 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,20166710):4300-4307 EI收录)

  [46] 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. EI收录)


专利 

[1] 邓晓刚,等。基于线性评价因子的线性-非线性工业过程故障检测方法.专利号:ZL201810679472.2,授权日期:2022.2.18.

[2] 邓晓刚,等,多变量工业过程故障分类方法.专利号:ZL2021105347212.0,授权日期:2022.4.19.

[3] 邓晓刚,等。 非线性化工过程故障检测方法.专利号:ZL202110533650.2,授权日期:2022.4.29.

[4] 邓晓刚,等。抽油机异常工况监控方法.专利号:ZL201910535088.X,授权日期:2022.7.12.

[5] 邓晓刚,等。模型预测控制器性能监控方法.专利号:ZL202010086630.0,授权日期:2021.1.22.

[6] 邓晓刚, . 基于主辅PCA模型的多变量工业过程故障检测方法. 专利号:201811503665.9,授权时间:2020.3.6.

[7] 邓晓刚,等. 基于偏FSELM的多变量工业过程故障分类方法.专利号:201811401207.4, 已授权,2019.11.29.

[8] 邓晓刚,等. 基于贝叶斯核慢特征分析的非线性工业过程故障检测方法.专利号:201710041421.2, 授权时间:2019.1.11.

[9] 邓晓刚,等. 基于核主元分析的非线性工业过程故障检测方法. 已授权。专利号:201710991994.1. 授权时间:2019.5.31.

[10] 邓晓刚,等。一种多变量工业过程故障识别方法。专利号:201510249620.3. 授权时间:2016.6.22.

[11] 邓晓刚,等。一种基于典型相异性分析的化工过程缓变故障检测方法.专利号:ZL 202210241086.1,申请日期:2022.3.11 (实审中)

[12] 邓晓刚,等。基于多块主成分分析网络的晶圆图缺陷检测方法.专利号:ZL202210424283.7,申请日期:2022.4.22 (实审中)

[13] 邓晓刚,等。一种基于小波格拉姆卷积神经网络的非视距信号识别方法.专利号:ZL202211105627.4,申请日期:2022.9.7 (实审中)