Generalized Dice's coefficient‐based multi‐block principal component analysis with Bayesian inference for plant‐wide process monitoring

Wang, Bei; Yan, Xuefeng; Jiang, Qingchao; Lv, Zhaomin

doi:10.1002/cem.2687

Cited by 29 publications

(18 citation statements)

References 47 publications

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“…Table summarizes the detailed monitoring results from the CMP‐PCA and CMP‐CFPCA. Besides, the results from other advanced methods are also cited for comparison, such as, FSCB, MGB‐PCA, and JIR‐PCA‐SVDD . Since these methods are modelled from normal samples, the outliers can be considered to have been removed.…”

Section: Methodsmentioning

confidence: 99%

Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component

Huang

Yan

2019

Can J Chem Eng

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In chemical process monitoring based on principal component analysis (PCA), sampling data with outliers and optimally select principal components are two challenging problems that have a main effect on monitoring performance. Given this situation, firstly, a novel outlier detection method, i.e., a robust CDC‐MVT‐PCA method (CMP), which integrates CDC‐MVT (the closest distance to centre and multivariate trimming) with PCA to identify and eliminate the outliers, is proposed to clean sample data. Secondly, based on the cleaning sample data, PCA is employed to obtain PCs. The cumulative frequency representing the variability of each PC is defined to find the optimal PCs, which are able to represent the current variability information. Finally, selecting optimal PCs online based on the cumulative frequency of each PC (CF‐PCA) is proposed to keep the most responsive components and, thus, to improve the monitoring performance. The effectiveness of the proposed robust fault monitoring algorithm is verified through a simple numerical simulation and the Tennessee Eastman process.

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Section: Methodsmentioning

confidence: 99%

Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component

Huang

Yan

2019

Can J Chem Eng

Self Cite

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“…In the SHC procedure figure, the values represent the average similarity between the rightward weight vector and the leftward weight vectors. For methods comparison, PCA, MGB‐PCA, and VWI‐MBPCA are used for the simulation process, and 99 % confidence limits (i.e. the significance level

α

is set as 0.01) are determined for monitoring statistics.…”

Section: Case Studymentioning

confidence: 99%

“…To further evaluate the proposed method, we apply five methods including PCA, DPCA, FSCB, MGB‐PCA, and the proposed VWI‐MBPCA to monitor the TE process. All the considered methods use a 99 % confidence limit as the fault detection threshold.…”

Section: Case Studymentioning

confidence: 99%

“…Recently, unlike these variable‐based block division methods, Wang et al proposed a novel multi‐block PCA method based on the generalized Dice coefficient (GDC) and Bayesian inference (MGB‐PCA), which applies the loading matrix‐based block division strategy. According to the literature, the loading matrix can provide inherent process information and is an efficient segmentation tool. However, there are some valuable problems deserving further studies.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

Wang

Deng

2017

Can J Chem Eng

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The traditional principal component analysis (PCA)‐based process monitoring method builds a global statistical model and omits the mining of the local variable behaviours, which may degrade the fault detection performance. Considering this problem, this paper proposes a variable weight information‐based multi‐block PCA (VWI‐MBPCA) method. Firstly, a sequence hierarchical clustering algorithm is proposed to divide the full PCA component space into several sub‐blocks, where the components sharing similar variable weight information are gathered together and then the sub‐block T2 statistic is constructed for monitoring sub‐block components. Further, the variables with small weight information on each component sub‐block are extracted to build an additional sub‐PCA model, where the trueTˆ2 statistic is developed to compensate the sub‐block T2 statistic. In order to integrate the monitoring results of each sub‐block, Bayesian inference is applied to construct an overall T2 statistic. To identify the faulty variables, a multi‐block PCA contribution plot is designed by choosing some specific blocks to highlight fault information. Finally, simulations on a numerical example and the benchmark Tennessee Eastman (TE) process are used to demonstrate the strengths of the proposed method.

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“…Consequently, Ge and Song proposed a distributed PCA model for plant‐wide process monitoring. Wang et al proposed Dice's coefficient‐based multi‐block PCA with Bayesian inference for process monitoring. Recently, Jiang and Yan proposed mutual information (MI)‐based multi‐block division.…”

Section: Introductionmentioning

confidence: 99%

Distributed process monitoring framework based on decomposed modified partial least squares

et al. 2019

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With the growing complexity of industrial processes, the scale of production processes tends to be large. The significant amount of measurement data in large‐scale processes poses challenges in data collection, management, and storage. In order to perform effective process monitoring in large‐scale processes, the distributed process monitoring strategy is widely applied. Meanwhile, product quality is an important indicator for industrial production. Therefore, a novel quality‐based distributed process monitoring scheme is proposed. Firstly, the Girvan‐Newman (GN) algorithm in complex network divides process variables into multiple sub‐blocks. Secondly, greedy algorithm‐based high‐dimensional mutual information (HDMI) is used to extract quality‐related variables in each sub‐block, through which the irrelevant and redundant variables are eliminated. Thirdly, the decomposed modified partial least squares (DMPLS) approach is used to detect whether a fault is quality‐related or not in each sub‐block. Finally, the Bayesian inference strategy is adopted to combine the detection results of all sub‐blocks. The effectiveness of the distributed DMPLS approach is illustrated through a numerical simulation and the Tennessee Eastman (TE) process. The results show the superiority of our proposed monitoring scheme.

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Generalized Dice's coefficient‐based multi‐block principal component analysis with Bayesian inference for plant‐wide process monitoring

Cited by 29 publications

References 47 publications

Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component

Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component

Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

Distributed process monitoring framework based on decomposed modified partial least squares

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