Dynamic principal component analysis(DPCA) is an extension of conventional principal component analysis(PCA) for dealing with multivariate dynamic data serially correlated in *** on the fact that the measured variable...
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Dynamic principal component analysis(DPCA) is an extension of conventional principal component analysis(PCA) for dealing with multivariate dynamic data serially correlated in *** on the fact that the measured variables in relation to chunk monitoring of the industrial fluidized-bed reactor are highly cross-correlated and auto-correlated, this paper presents a practical strategy for chunk monitoring by adopting DPCA in order to overcome the shortcomings of the conventional *** introducing the basic principle of DPCA, both how to determine the time lagged length of data matrix and how to calculate the nonparametric control limits when the dynamic data are not subject to the assumption of independently identically distribution(IID) were *** appropriate DPCA model based on the real data from a industrial fluidized-bed reactor was built, with parallel analysis and empirical reference distribution(ERD)method to select time lagged length and control limits, *** data pretreatment, data smoothing was used to reduce noise and the serial correlations to some *** simulation test results showed the effectiveness of the DPCA based method.
针对化工生产中日益增多的间歇过程,提出了一种基于多元统计信号处理的过程监控方法,其主要思想为将过程信息空间划分为由盲源信号描述的信号子空间、过程主元描述的信号子空间和残差信号子空间,随后对各个信号子空间构造过程统计量或分类器提取信号特征进行过程监控,该方法避免了传统多元统计过程控制(mult-ivariate statistical process contro,lMSPC)需假设过程特征信号服从正态分布的前提.将本方法与传统MSPC方法的性能进行了对比,并在仿真中给出了对比研究结果.通过对间歇过程的仿真研究表明,该方法不仅能够有效地检测出故障,而且有利于故障的分离和定位,从而说明该方法不仅是有效的,而且其性能优于仅能检测故障的传统MSPC过程监控方法.
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