欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
主元分析是基于独立元分析过程监控中一种重要而且常用的白化方法,可以有效地降低监控对象的维数。其基于正常样本数据,根据主元方差贡献率选取主元,保留正常样本中的大部分方差信息,消除噪声。在PCA模型中,每个主元的T2统计量表征着样本数据沿该主元方向的变异程度。通过对故障样本数据每个主元的T2统计量分析,发现某些故障信息投影在方差较小且被舍弃的主元上,从而造成故障信息的损失,进而影响了ICA的监控性能,造成故障的漏检和故障源的误识别。最后,采用一个简易系统和TE过程,验证了PCA白化过程对ICA监控性能的影响。
英文摘要：
Principal component analysis（PCA） was an important and most widely used data whitening approach in process monitoring based on independent component analysis（ICA） due to its effectivity in reducing dimensions of objects.PCA model was generated based on sample data of normal process,and the first several PCs which contain the most variance information of normal process were employed for ICA and process noise was eliminated.In PCA model,T 2 statistic of each principal component has the property that it can measure variation along direction of the component.By researching the T 2 statistic of sample data of fault process,it is found that information of some faults are mostly reflected on the components corresponding to smaller variance contribution,which are regarded as process noise and eliminated,and thus missed detection problem is happened.At last,the effect of PCA whitening on chemical process monitoring based on PCA-ICA is illustrated through simulations of both a simple process and TE process,and the results prove the proposed opinion.
同期刊论文项目
同项目期刊论文
Self-adaptive Co-evolution differential evolution algorithm for optimal design of water distribution
Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated wi
Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant
Transforming input variables for RBFN based on PCA-ASH multivariate correlation analysis and its app
Selection and transformation of input variables for RVM based on MIPCAMI and 4-CBA concentration mod
Simulation and analysis of dehydration distillation column based on distillation mechanism integrate
Distributed Statistical Process Monitoring Based on Four-Subspace Construction and Bayesian Inferenc
Optimization of RBF Neural Networks Using a Rough K-Means Algorithm and Application to Naphtha Dry P
Multi-Block Independent Component Analysis Integrated with Hellinger Distance and Bayesian Inference
Self-adaptive Differential Evolution Algorithm with Zoning Evolution ofControl Parameters and Adapti
Multi-objective modified differential evolution algorithm with archive-base mutation for solving mul
Optimization of a Multilayer Neural Network by Using Minimal Redundancy Maximal Relevance-Partial Mu
Double-step Block Division Plant-Wide Fault Detection and Diagnosis Based on Variable Distribution a
Batch Process Monitoring Based on Multisubspace Multiway Principal Component Analysis and Time-Serie
Batch process monitoring based on just-in-time learning and multiple-subspace principal component an
Modified Radial Basis Function Neural Network Integrated with Multiple Regression Analysis And Its A
Global best harmony search algorithm with control parameters co-evolution based on PSO and its appli
Statistical process monitoring based on improved principal component analysis and its application to
Hybrid Model of Industrial p-Xylene Oxidation Incorporated Fractional Kinetic Model with Intelligent
Weighted kernel principal component analysis based on probability density estimation and moving wind
Chemical process dynamic optimization based on the differential evolution algorithm with an adaptive
Independent component analysis-based non-Gaussian process monitoring with preselecting optimal compo
Reservoir Computing with Sensitivity Analysis Input Scaling Regulation and Redundant Unit Pruning fo
Fault detection and isolation in hybrid process systems using a combined data-driven and observer-de
Improved simple deterministically constructed Cycle Reservoir Network with Sensitive Iterative Pruni
Fault detection and identification using a Kullback-Leibler divergence based multi-block principal c
Chaotic Differential Evolution Algorithm Based on Competitive Coevolution and Its Application to Dyn
Fault detection in non-Gaussian processes based on mutual information weighted independent component
Modified kerenel regression integrated with monotonicity knowledge and its application to chemical e
Burning side reaction model of the INVISTA oxidation process using a radial basis function neural ne
Differential evolution algorithm with self-adaptive strategy and control parameters for P-xylene oxi
Harmony search algorithm with differential evolution based control parameter co-evolution and its ap
Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based mu
Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organiz
A comparative study of hybrid models combining various kinetic and regression models for p-xylene ox
Estimating the chemical reaction kinetics of p-xylene oxidation using artificial neural network with
Joint probability density and double weighted independent component analysis for Multimode Non-Gauss
Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic
Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angul
Hybrid model for main and side reactions of p-xylene oxidation with prior knowledge based monotone a
Probabilistic monitoring of chemical processes using adaptively weighted factor analysis and its app
Non-Gaussian chemical process monitoring with adaptively weighted independent component analysis and
期刊信息
位置：