Nonlinear Process Monitoring Based on Multi-block Dynamic Kernel Principal Component Analysis

Deng, Jiawei; Deng, Xiaogang; Wang, Lei

doi:10.1109/wcica.2018.8630617

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…In order to perform block division when process knowledge is not available, Jiang and Yan [327] proposed to use mutual information (MI) based clustering. This idea was fused with kernel PCA based process monitoring by Jiang and Yan [180], Huang and Yan [245], and Deng et al [287]. All these works have used the TEP as a case study, and they have consistently elucidated 4 sub-blocks for the TEP.…”

Section: Multi-block and Distributed Monitoringmentioning

confidence: 99%

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.

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Section: Multi-block and Distributed Monitoringmentioning

confidence: 99%

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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“…The segmentation of blocks in distributed alarm systems is an important part. − The initial block segmentation is directly based on process knowledge. MacGregor et al achieved independent alarm analysis by establishing a latent variable (LV) space for each sub-block .…”

Section: Introductionmentioning

confidence: 99%

Novel Distributed Alarm Visual Analysis Using Multicorrelation Block-Based PLS and Its Application to Online Root Cause Analysis

Zhu

Luo

2019

Ind. Eng. Chem. Res.

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Process monitoring and alarm systems play an important role in production safety. Distributed alarm systems have been utilized to effectively reduce the burden of data calculations, contributing to fast-track root causes of alarms. However, the traditional distributed contribution analysis method cannot be well applied to online monitoring in time. In addition, the correlation-based variable reconstruction method cannot accurately explain the actual process. To solve this problem, a novel distributed alarm analysis model based on multicorrelation blocks and an improved contribution graph is proposed. The proposed multicorrelation blocks are based on partial least squares. The computational burden is reduced, but accuracy is ensured by multicorrelation block partial least squares. In addition, an improved alarm analysis strategy using the contribution value is adopted, so that the results of the distributed contribution analysis method can be used for essential variable analysis online. Simulation results of a case study using the Tennessee Eastman process verify the effectiveness and practicability of the proposed method and indicate that the online analysis performance in terms of actual industrial process monitoring and root cause analysis is improved.

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Nonlinear Process Monitoring Based on Multi-block Dynamic Kernel Principal Component Analysis

Cited by 2 publications

References 12 publications

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Novel Distributed Alarm Visual Analysis Using Multicorrelation Block-Based PLS and Its Application to Online Root Cause Analysis

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