Double‐step block division plant‐wide fault detection and diagnosis based on variable distributions and relevant features

Huang, Jian; Yan, Xuefeng

doi:10.1002/cem.2743

Cited by 24 publications

(18 citation statements)

References 41 publications

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“…However, when the data in each sub‐block follow a non‐Gaussian distribution, the above method cannot solve the problem. Huang and Yan studied a two‐step block separation fault detection and diagnosis method. In the first step, variables were divided into Gaussian and non‐Gaussian parts.…”

Section: Introductionmentioning

confidence: 99%

Process monitoring method based on correlation variable classification and vine copula

Cui

2020

Can J Chem Eng

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Chemical processes are becoming increasingly complicated, leading to an increase in process variables and more complex relationships among them. The vine copula has a significant advantage in portraying the dependence of high-dimensional variables. However, as the dimensions increase, the vine copula model incurs a high computational load; such pressure greatly reduces model efficiency. Relationships among variables in the industrial process are complex. Different variables may be strongly or weakly associated or even independent. This paper proposes a process monitoring method based on correlation variable classification and vine copula. The weighted correlation measure is first used to divide variables into a correlated subspace and weakly correlated subspace. Then, two vine structures, C-vine and D-vine, are applied to the correlated and weakly correlated subspaces, respectively. This method takes advantage of C-vine for correlated variables and the flexibility of D-vine for weakly correlated variables. Finally, comprehensive statistics are established based on different subspaces. Monitoring results of the numerical system and the Tennessee Eastman process demonstrate the effectiveness and validity of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Process monitoring method based on correlation variable classification and vine copula

Cui

2020

Can J Chem Eng

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“…In this circumstance, data‐driven multivariate statistical process monitoring (MSPM) methods have also become popular considering its convenience and relaxing of prior knowledge . Principal component analysis (PCA), independent component analysis (ICA), and partial least squares (PLS) are the basic MSPM methods. Numerous improvements to these methods have been developed to solve diverse problems.…”

Section: Introductionmentioning

confidence: 99%

Quality‐relevant dynamic process monitoring based on mutual information multiblock slow feature analysis

Zheng

Jiang

Yan

2019

Journal of Chemometrics

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Slow feature analysis (SFA) is an efficient technique in exploring process dynamic information and is suitable for quality‐relevant process monitoring. However, involving quality‐irrelevant variables or features may introduce redundant information and degrade the monitoring performance. A novel multiblock monitoring scheme based on mutual information (MI) and SFA is proposed to detect an efficient quality‐relevant fault for dynamic processes. First, all process variables are divided into two blocks in accordance with their MI values with quality variables. Second, slow features (SFs) of two blocks are extracted via the SFA. The SFs, which are extracted from the quality‐relevant variables, are not all related to the quality variable. Thus, these SFs are further partitioned into two blocks in accordance with their MI values with quality variables. The SFs from three blocks are obtained, and monitoring statistics are constructed. Two simulation studies, including a numerical example and Tennessee Eastman process, demonstrate that the proposed method outperforms conventional methods.

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“…Tong et al proposed a novel multi‐block method by constructing four subspaces and using the Bayesian inference to obtain fusion monitoring statistics (FSCB). Some other variable block division‐based multi‐block PCA methods can be seen in the literature …”

Section: Introductionmentioning

confidence: 99%

“…Two monitoring statistics are usually constructed for fault detection, which are defined as follows:

T^{2} = t_{k}^{normalT} Σ^{- 1} t_{k},

S P E = {‖ x - P_{k} {P_{k}}^{normalT} x^{} ‖}^{2} = t_{r}^{normalT} t_{r},

where

Σ^{}

is a diagonal matrix with its elements as eigenvalues

{λ_{1}, λ_{2}, \dots, λ_{k}}

. The control limits of these two monitoring statistics can be computed by referring to the related literature …”

Section: Introductionmentioning

confidence: 99%

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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Double‐step block division plant‐wide fault detection and diagnosis based on variable distributions and relevant features

Cited by 24 publications

References 41 publications

Process monitoring method based on correlation variable classification and vine copula

Process monitoring method based on correlation variable classification and vine copula

Quality‐relevant dynamic process monitoring based on mutual information multiblock slow feature analysis

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

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