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基于多元统计分析的过程监测研究 学位论文
硕士, 沈阳: 中国科学院沈阳自动化研究所, 2017
Authors:  王中伟
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主元分析方法  支持向量数据描述  谱聚类  多模态过程监测  局部-全局模型  
Monitoring based on MIC-PCA and SVDD for industrial process 会议论文
Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017, Chongqing, China, March 25-26, 2017
Authors:  Wang ZW(王中伟);  Li S(李帅);  Song H(宋宏);  Zhou XF(周晓锋)
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Principal Component Analysis  Support Vector Data Description  Maximal Information Coefficient  Process Monitoring  Industrial Process  
基于对数变换和最大信息系数PCA的过程监测 期刊论文
科学技术与工程, 2017, 卷号: 17, 期号: 16, 页码: 259-265
Authors:  王中伟;  宋宏;  李帅;  周晓锋
Adobe PDF(549Kb)  |  Favorite  |  View/Download:184/24  |  Submit date:2017/07/17
主元分析方法  最大信息系数  对数变换  过程监测  
Correlated and weakly correlated fault detection based on variable division and ICA 期刊论文
Computers and Industrial Engineering, 2017, 卷号: 112, 页码: 320-335
Authors:  Li S(李帅);  Zhou XF(周晓锋);  Pan FC(潘福成);  Shi HB(史海波);  Li KT(李开拓);  Wang ZW(王中伟)
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Fault Detection  Monitoring  Variable Division  Correlated And Weakly Correlated Variables  Independent Component Analysis  
Multimode processes monitoring using global–local MIC-PCA-SVDD 会议论文
Lecture Notes in Electrical Engineering, Kunming, China, July 10-12, 2017
Authors:  Li S(李帅);  Zhou XF(周晓锋);  Shi HB(史海波);  Wang ZW(王中伟)
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Monitoring  Multimode processes  Multimode division  Global–local MIC-PCA-SVDD  
Fault detection based on global-local PCA-SVDD for multimode processes 会议论文
Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017, Kunming, China, July 10-12, 2017
Authors:  Li S(李帅);  Zhou XF(周晓锋);  Shi HB(史海波);  Wang ZW(王中伟)
Adobe PDF(594Kb)  |  Favorite  |  View/Download:111/28  |  Submit date:2018/08/12
fault detection  monitoring  multimode industrial processes  mode division  global-local PCA-SVDD