In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component a...In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.展开更多
针对复杂工业过程中的多工况和非高斯信息问题,提出一种基于外部分析的ICA-PCA(independentcomponent analysis and principal component analysis)在线统计监测新方法。首先把过程变量分为外部变量和主要变量,通过偏最小二乘(PLS)回归...针对复杂工业过程中的多工况和非高斯信息问题,提出一种基于外部分析的ICA-PCA(independentcomponent analysis and principal component analysis)在线统计监测新方法。首先把过程变量分为外部变量和主要变量,通过偏最小二乘(PLS)回归方法分离外部变量对主要变量的影响,然后利用ICA-PCA两步信息提取策略,完整地提取过程的信息,最后用3个统计量对过程进行监测,建立了一种具有非高斯特性的多工况过程在线监测算法。通过对一个数值例子和连续搅拌槽(CSTR)过程的仿真研究,说明提出的方法是可行、有效的。展开更多
针对复杂工业过程中的非线性、非高斯特性以及多工况问题,提出了一种基于局部模型的在线统计监测新方法.首先利用局部最小二乘支持向量机回归(Least square support vector regression,LSSVR)模型对过程输出进行预测,与真实的输出相比...针对复杂工业过程中的非线性、非高斯特性以及多工况问题,提出了一种基于局部模型的在线统计监测新方法.首先利用局部最小二乘支持向量机回归(Least square support vector regression,LSSVR)模型对过程输出进行预测,与真实的输出相比较构成残差序列.然后利用ICA-PCA两步特征提取策略,完整地提取残差的高斯和非高斯信息,最后用三个统计量(I^2、T^2和SPE)对过程进行监测,建立了一种具有非线性、非高斯特性的多工况过程在线监测算法.通过对TE(Tennessee Eastman)过程的仿真研究,验证提出的方法是可行、有效的,并显示出了一定的故障检测能力.展开更多
基金Project (No. 60774067) supported by the National Natural ScienceFoundation of China
文摘In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.
文摘针对复杂工业过程中的多工况和非高斯信息问题,提出一种基于外部分析的ICA-PCA(independentcomponent analysis and principal component analysis)在线统计监测新方法。首先把过程变量分为外部变量和主要变量,通过偏最小二乘(PLS)回归方法分离外部变量对主要变量的影响,然后利用ICA-PCA两步信息提取策略,完整地提取过程的信息,最后用3个统计量对过程进行监测,建立了一种具有非高斯特性的多工况过程在线监测算法。通过对一个数值例子和连续搅拌槽(CSTR)过程的仿真研究,说明提出的方法是可行、有效的。
