1987.9-1991.7 东北大学 自动控制系 工业自动化仪表专业 学士学位 1991.9-1994.3 东北大学自动控制系自动化仪表及装置专业硕士学位 1998.9-2002.9东北大学信息科学与工程学院检测技术与自动化装置专业博士学位

1994.3-1997.6 东北大学 自动控制系 助教 1997.6-2002.6 东北大学 自动控制系 讲师 2002.6至今东北大学信息科学与工程学院副教授

项目 1.基于工业大数据的铝-铜板带材智能化工艺控制技术，国家重点研发计划项目，2017YFB0306400，2017.07- 2021.06，1924万元 2.长型材智能化制备关键技术，国家重点研发计划项目，2017YFB0304200， 2017.07- 2021.06，2469万元 3.工业炉窑过程建模与质量控制研究，中央高校基本科研业务费，N100404018，2011.01- 2012.12，8万元 4.流形学习和半监督SVM新算法用于复杂工业过程故障诊断的研究，国家自然科学基金，610ᠦ୔

期刊论文 1.Yang Y, Chen X, Zhang Y, et al. A Novel Decentralized Weighted ReliefF-PCA Method for Fault Detection[J]. IEEE Access, 2019, 7: 140478-140487. 2.Yang Y, Pan Y, Zhang L, et al. Incipient Fault Detection Method Based on Stream Data Projection Transformation Analysis[J]. IEEE Access, 2019, 7: 93062-93075. 3.Yang Y, Wang X, Liu X. A new incipient fault monitoring method based on modified principal component analysis[J]. Journal of Chemometrics, 2019, 33(10): e3175. 4.Yang Y, Shi X, Liu X, et al. A novel MDFA-MKECA method with application to industrial batch process monitoring[J]. IEEE/CAA Journal of Automatica Sinica, 2019:1-9. 5.杨英华, 石翔, 李鸿儒. 基于数据特征的加热炉钢温预报模型[J]. 东北大学学报(自然科学版), 2019, 40(3): 305-309. 6.Yang Y H, Li X L, Liu X Z, et al. Wavelet kernel entropy component analysis with application to industrial process monitoring[J]. Neurocomputing, 2015, 147: 395-402. 7.李召, 杨英华, 李智辉. 基于小波去噪结合CVA-ICA的故障检测方法的研究[J]. 仪表技术与传感器, 2014, 4: 80-84. 8.杨英华, 魏玉龙, 李召, 秦树凯. 基于子空间混合相似度的过程监测与故障诊断[J]. 仪器仪表学报, 2013, 34(04): 935-941. 9.Yang Y H, Chen Y L, Chen X B, et al. Multivariate industrial process monitoring based on the integration method of canonical variate analysis and independent component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2012, 116: 94-101. 10.杨英华, 李召, 陈永禄, 陈晓波. 基于CVA-ICA与CSM的故障诊断方法[J]. 东北大学学报(自然科学版), 2012, 33(12): 1685-1689. 会议论文 1.Yinghua Yang; Yongkang Pan; Liping Zhang, Fault Monitoring Method Based on Mutual Information and Relative Principal Component Analysis, Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019, 2019:440-444 2.Yinghua Yang; Xiulong Wang, Fault diagnosis optimization algorithm based on k nearest neighbor, Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019, 2019:457-461 3.Yinghua Yang; Guoqiang Shi; Xiang Shi, Fault monitoring and classification of rotating machine based on PCA and KNN, Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018, 2018:1795-1800 4.Yinghua Yang; Yuan Lu, Process monitoring and fault diagnosing of TE process based on the integration method of PCA and cluster analysis, Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018, 2018:3227-3231 5.Yinghua Yang; Xiang Shi; Shuang Yi; Xiaobo Chen; Shukai Qin, A novel process monitoring method based on improved DTW-MKECA, Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017, 2017:4187-4192 6.Yang Yinghua; Li Huaqing; Li Chenlong; Qin Shukai; Chen Xiaobo, Kernel entropy component analysis based process monitoring method with process subsystem division, Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015, 2015:2684-2688 7.Yang Yinghua; Yu Qingchao; Qin Shukai, Fault identification for industrial process based on KPCA-SSVM, Recent Advances in Computer Science and Information Engineering, RACSIE2012, 2012:63-69 8.Yang Yinghua; Chen Yonglu; Chen Xiaobo; Qin Shukai, Multivariate statistical process monitoring and fault diagnosis based on an integration method of PCA-ICA and CSM, Green Power, Materials and Manufacturing Technology and Applications, GPMMTA2011, 2011:110-114 9.Yang Yinghua; Peng Lin; Chen Xiaobo; Liu Xiaozhi, A GA-BP neural network model for predicting the temperature of slabs in the reheating furnace, Information Technology for Manufacturing Systems II, ITMS2011, 2011:1371-1377 10.Yang Yinghua; Tang Zhenhao; Chen Xiaobo, Research on a new method of subpixel location using GA, Proceedings 2010 6th International Conference on Natural Computation, ICNC 2010, 2010:4219-4223

专著或教材 1. 控制系统分析与设计-过程控制系统，清华大学出版社，2014, ISBN:978-7-302-33320-3,全国控制工程专业学位教育指导委员会推荐教材，参编字数:12.8万.专利 1.发明专利：电弧炉熔炼阶段判定方法及系统，CN 104531947B 2. 软件著作权：加热炉虚拟系统软件V1.0，软著登记号：2019SR0825105