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RISS 인기검색어
- 검색키워드
- 주제어 : Plant-wide Process Monitoring (검색결과 3 건)
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Two-level multiblock statistical monitoring for plant-wide processes
Zhiqiang Ge,Zhihuan Song 한국화학공학회 2009 Korean Journal of Chemical Engineering Vol.26 No.6
Due to the complexity of plant-wide processes, many of the current multivariate statistical process monitoring techniques are lacking in interpretation of the detected fault, and fault identification also becomes difficult. A new two-level multiblock independent component analysis and principal component analysis (MBICA-PCA) method is proposed in this paper. Different from the conventional method, the new approach can incorporate block information into the high level for global process monitoring. Through the new method, the process monitoring task can be greatly reduced and the interpretation for the process can be made more quickly. When a fault is detected, a two-step fault identification method is proposed. The responsible sub-block is first identified by contribution plots, which is followed by fault reconstruction in the corresponding sub-block for advanced fault identification. A case study of the Tennessee Eastman (TE) process evaluates the feasibility and efficiency of the proposed method.
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Sparse probabilistic principal component analysis model for plant-wide process monitoring
Jing Zeng,Kangling Liu,Weiping Huang,Jun Liang 한국화학공학회 2017 Korean Journal of Chemical Engineering Vol.34 No.8
In the industrial monitoring process, probabilistic principal component analysis (PPCA) is a popular algorithm for reducing the dimension. However, the principal components (PCs) are not easy to interpret and its preserved number cannot be determined automatically. In this paper, we propose a sparse PPCA (SPPCA) to improve the interpretability by adding a prior and introducing sparsification to the loading matrix of PPCA. An expectation-maximization (EM) algorithm is used to obtain the parameters of the probabilistic formulation, and the dimensionality of the latent variable space can be automatically determined during the iterative process. With the sparse representation, a process monitoring strategy is then developed with the construction of several partial PPCA models. Case studies of SPPCA to a numerical case and Tennessee Eastman (TE) benchmark process demonstrate its feasibility and efficiency.
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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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