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【期刊论文】Two-Dimensional Dynamic PCA for Batch Process Monitoring
陆宁云, Ningyun Lu, Yuan Yao, Furong Gao, Fuli Wang
AIChE Journal December 2005 Vol. 51, No. 12,-0001,():
-1年11月30日
dynamic principal component analysis, 2-D modeling, batch processes, process monitoring, statistical analysis
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【期刊论文】Sub-PCA Modeling and On-line Monitoring Strategy for Batch Processes
陆宁云, Ningyun Lu, Furong Gao, Fuli Wang
AIChE Journal January 2004 Vol. 50, No. 1,-0001,():
-1年11月30日
batch process, online monitoring, PCA, chromatography, statistical analysis
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65浏览
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陆宁云, Ningyun Lu, , Fuli Wang, Furong Gao
Ind. Eng. Chem. Res., Vol. 42, No. 18, 2003, 4198-4207,-0001,():
-1年11月30日
Product quality and operation safety are important aspects of industrial processes, particularly those with large numbers of correlated process variables. Principal component analysis(PCA) has been widely used in multivariate process monitoring for its ability to reduce process dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults with similar time-domain process characteristics. A wavelet-based time-frequency approach is developed in this paper to improve PCA-based methods by extending the time-domain process features into time-frequency information. Subsequently, a similarity measure is presented to compare process features for on-line process monitoring and fault diagnosis. Simulation results show that the proposed multivariate time-frequency process feature is effective in both fault detection and diagnosis, illustrating the potentials for real-world application.
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陆宁云, , 王福利, 高福荣, 王姝
自动化学报2006年5月第32卷第3期/ACTA AUTOMATICA SINICA May,2006, Vol. 32, No. 3,-0001,():
-1年11月30日
现代过程工业正逐渐倚重于生产小批量、多品种、高附加值产品的间歇过程。基于多元统计模型的过程监测是保障生产安全和产品质量的重要工具,从间歇过程独特的数据特性出发,将现有的多元统计建模方法进行合理的分类,简要回顾了各类方法的起源、发展及延伸的历程,除了阐述每种方法的基本原理,还详细讨论了各种方法的适用背景,相互关联及优缺点等内容,并对这一领域中依然存在的问题以及研究前景给出中肯的评述。
间歇过程, 多元统计模型, 过程监测, 主成分分析, 偏最小二乘, 三线性分解模型
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【期刊论文】Stage-Based Process Analysis and Quality Prediction for Batch Processes
陆宁云, Ningyun Lu, Furong Gao
Ind. Eng. Chem. Res., Vol. 44, No. 10, 2005, 3547-3555,-0001,():
-1年11月30日
A process analysis and quality prediction scheme is proposed based on a stage-based PLS modeling for batch processes. Without any requirement of prior process knowledge, the scheme first divides a batch process into stages of different process characteristics. Subsequently, a strategy is developed to identify stages that have critical influences on concerned qualities, defined as critical-to-quality stages. Within these critical-to-quality stages, an algorithm is then further developed to identify the variables that have significant contributions to the quality variations. Finally, based on the identified nature of quality and stage relationships, a set of algorithms is developed for online quality prediction. The applications of the proposed scheme to injection molding show that the proposed analysis and quality prediction are not only effective but are also able to enhance process understanding and identify specific variables and periods for quality improvement.
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