Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA

Deng, Xiaogang; Deng, Jiawei

doi:10.1021/acs.iecr.8b04794

Cited by 35 publications

(25 citation statements)

References 61 publications

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“…The clustering results via KECA are shown in Table 2 . As we know, KPCA and KECA are very similar [ 12 , 33 ] dimension reduction methods. In order to verify the effectiveness of KECA, the clustering results via KPCA are shown in Table 3 .…”

Section: Methodsmentioning

confidence: 99%

State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance

Wang

2019

Entropy

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In the hot strip rolling process, many process parameters are related to the quality of the final products. Sometimes, the process parameters corresponding to different steel grades are close to, or even overlap, each other. In reality, locating overlap regions and detecting products with abnormal quality are crucial, yet challenging. To address this challenge, in this work, a novel method named kernel entropy component analysis (KECA)-weighted cosine distance is introduced for fault detection and overlap region locating. First, KECA is used to cluster the training samples of multiple steel grades, and the samples with incorrect classes are seen as the boundary of the sample distribution. Next, the concepts of recursive-based regional center and weighted cosine distance are introduced. For each steel grade, the regional center and the weight coefficients are determined. Finally, the weighted cosine distance between the testing sample and the regional center is chosen as the index to judge abnormal batches. The samples in the overlap region of multiple steel grades need to be focused on in the real production process, which is conducive to quality grade and combined production. The weighted cosine distances between the testing sample and different regional centers are used to locate the overlap region. A dataset from a hot steel rolling process is used to evaluate the performance of the proposed methods.

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Section: Methodsmentioning

confidence: 99%

State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance

Wang

2019

Entropy

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“…Data based approaches are more suitable for chemical and oil refining processes, since a large number of real operating data are collected every few minutes. Because process variables are high dimensional, multivariate dimensionality reduction and feature extraction methods are widely popular, such as principal component analysis (PCA) [17]- [21], independent component analysis (ICA) [22]- [25], and slow feature analysis (SFA) [26], [27]. Standard PCA, ICA and SFA are data processing methods for one variable set.…”

Section: Introductionmentioning

confidence: 99%

Variable Sub-Region Canonical Variate Analysis for Dynamic Process Monitoring

Cao

Deng

et al. 2020

IEEE Access

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When all process variables are used to establish one data-based model, some variables have little contributions to the model. When these variables are grouped into a small local variable set to develop a data-based model, their contributions will become large. It is called local variable characteristic. Standard canonical variate analysis (CVA) based process monitoring method doesn't consider local process variable characteristic. To solve this problem, a variable sub-region based canonical variate analysis (V-CVA) is proposed for dynamic process monitoring by enhancing the information of local variables. The proposed method combines variable sub-region division and Bayesian fusion. First, standard CVA is performed by using all process variables. Then, K-means clustering is adopted to divide process variables into sub-regions based on the mutual information of process variables and canonical variables. For each variable sub-region, CVA is conducted to compute local process monitoring statistics. Finally, local statistics are fused to build ensemble statistics based on the idea of Bayesian inference. Compared with the standard CVA method, the proposed V-CVA method can emphasize the information of local variables, and has better monitoring performance in the canonical variable feature subspace and the prediction residual subspace. Two examples demonstrate the effectiveness of the proposed method.

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“…The parameters of the temperature profile cannot be measured directly and need to be estimated. The PCA‐based estimation design methods have been widely used in the chemical industry . However, the standard PCA method is not designed to include priori selections.…”

Section: Introductionmentioning

confidence: 99%

“…The PCA-based estimation design methods have been widely used in the chemical industry. [41][42][43][44] However, the standard PCA method is not designed to include priori selections. For example, the temperature sensitive stage must be preselected.…”

Section: Introductionmentioning

confidence: 99%

Temperature Inferential Control of Heat‐Integrated Distillation Column Based on Variable Sensitive Stage Temperature Set‐point

Cong

Liu

2019

Can J Chem Eng

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Heat‐integrated distillation is an improved distillation technique with remarkable energy‐saving potential. A control scheme with a variable sensitive stage temperature set‐point is proposed to solve the control problem of a heat‐integrated distillation column (HIDiC). An online estimator is designed to support the variation of the set‐point. The locations of the stage temperature measurements are carefully selected based on a combination strategy with three steps. First, the sensitive stages are selected. Then, the following stages are determined by a PCA‐based method. Finally, a maximum differentiation method provides the remaining measurement selections. According to the profile parameters estimated by the proposed estimator, the set‐point of the sensitive stage temperature is adjusted adaptively to reduce the influence of the disturbances. Two commonly‐used PID controllers, the sensitive temperature control and the temperature differential control, are developed as the comparative study. The simulation results show that the proposed control scheme has a distinct advantage in restraining different disturbances.

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Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA

Cited by 35 publications

References 61 publications

State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance

State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance

Variable Sub-Region Canonical Variate Analysis for Dynamic Process Monitoring

Temperature Inferential Control of Heat‐Integrated Distillation Column Based on Variable Sensitive Stage Temperature Set‐point

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