针对工业实时数据库在某石化企业中的应用,提出了一套实时数据库与DCS及W eb界面之间的接口方案.该方案是在W indow s 2000 Server操作系统上,应用了W IN 32中的多线程、匿名管道、W inSock等技术实现的.长时间的现场运行证明,该方案是...针对工业实时数据库在某石化企业中的应用,提出了一套实时数据库与DCS及W eb界面之间的接口方案.该方案是在W indow s 2000 Server操作系统上,应用了W IN 32中的多线程、匿名管道、W inSock等技术实现的.长时间的现场运行证明,该方案是可靠、稳定和有效的,达到了系统设计目标.展开更多
Dynamic principal component analysis(DPCA) is an extension of conventional principal component analysis(PCA) for dealing with multivariate dynamic data serially correlated in time.Based on the fact that the measured v...Dynamic principal component analysis(DPCA) is an extension of conventional principal component analysis(PCA) for dealing with multivariate dynamic data serially correlated in time.Based 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 method.After 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 discussed.An 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, respectively.During data pretreatment, data smoothing was used to reduce noise and the serial correlations to some degree.The simulation test results showed the effectiveness of the DPCA based method.展开更多
文摘针对工业实时数据库在某石化企业中的应用,提出了一套实时数据库与DCS及W eb界面之间的接口方案.该方案是在W indow s 2000 Server操作系统上,应用了W IN 32中的多线程、匿名管道、W inSock等技术实现的.长时间的现场运行证明,该方案是可靠、稳定和有效的,达到了系统设计目标.
文摘Dynamic principal component analysis(DPCA) is an extension of conventional principal component analysis(PCA) for dealing with multivariate dynamic data serially correlated in time.Based 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 method.After 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 discussed.An 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, respectively.During data pretreatment, data smoothing was used to reduce noise and the serial correlations to some degree.The simulation test results showed the effectiveness of the DPCA based method.
