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- 저자 : Qingchao Jiang (검색결과 3 건)
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Improved fault detection in nonlinear chemical processes using WKPCA-SVDD
Qingchao Jiang,Xuefeng Yan 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.11
Conventional kernel principal component analysis (KPCA) does not always perform well for nonlinearprocess monitoring because the beneficial information for fault detection may be submerged under the retained kernelprincipal components (KPCs). To overcome this deficiency, an adaptively weighted KPCA integrated with supportvector data description (WKPCA-SVDD) monitoring method is proposed. In WKPCA-SVDD, the importance of eachKPC is evaluated online by the change rate of T2statistic and then distinguished weighting values are set on the KPCs. The behaviors of all KPCs are comprehensively evaluated by the SVDD technique. Since the beneficial informationis highlighted, the monitoring performance of the statistic in the dominant subspace can be improved. The proposedWKPCA-SVDD is applied to both a numerical process and the complicated Tennessee Eastman benchmark process. Monitoring results have indicated the efficiency of the WKPCA-SVDD method.
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Xuefeng Yan,Qingchao Jiang,Zhaomin Lv,Meijin Guo 한국화학공학회 2013 Korean Journal of Chemical Engineering Vol.30 No.6
Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And,the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring.
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Bei Wang,Xuefeng Yan,Qingchao Jiang 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.6
Considering the huge number of variables in plant-wide process monitoring and complex relationships(linear, nonlinear, partial correlation, or independence) among these variables, multivariate statistical process monitoring(MSPM) performance may be deteriorated especially by the independent variables. Meanwhile, whether related variableskeep high concordance during the variation process is still a question. Under this circumstance, a multi-block technologybased on mathematical statistics method, Kullback-Leibler Divergence, is proposed to put the variables having similarstatistical characteristics into the same block, and then build principal component analysis (PCA) models in each lowdimensionalsubspace. Bayesian inference is also employed to combine the monitoring results from each sub-blockinto the final monitoring statistics. Additionally, a novel fault diagnosis approach is developed for fault identification. The superiority of the proposed method is demonstrated by applications on a simple simulated multivariate processand the Tennessee Eastman benchmark process.
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