Fault Detection in Chemical Processes Using Discriminant Analysis and Control Chart

Pei, Xudong; Yamashita, Yoshiyuki; Yoshida, Masatoshi; Matsumoto, Shigeru

doi:10.1252/jcej.07we088

Cited by 13 publications

(3 citation statements)

References 33 publications

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“…Discriminant Analysis and Control Chart" by Xudong Pei, Yoshiyuki Yamashita, Masatoshi Yoshida and Shigeru Matsumoto, Tohoku University (Pei et al, 2008) Citation: This paper presents a new fault detection methodology based on the Fisher discriminant analysis (FDA) and individuals control charts (XmR control charts). The performance of the proposed method is verified through application to the TennesReceived on June 18, 2009.…”

Section: "Fault Detection In Chemical Processes Usingmentioning

confidence: 99%

JCEJ Outstanding Paper Award of 2008

Kosuge

Ohmura

2009

J. Chem. Eng. Japan

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Section: "Fault Detection In Chemical Processes Usingmentioning

confidence: 99%

JCEJ Outstanding Paper Award of 2008

Kosuge

Ohmura

2009

J. Chem. Eng. Japan

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“…Data-based methods are mainly divided into two types: univariate tools and multivariate tools. , Both of these families of data-driven models use historical process data collected in normal operating condition (NOC) to define the control limits (CLs) of acceptable process operations. The Shewhart chart, exponentially weighted moving average (EWMA) chart, and cumulative sum (CUSUM) chart are the widely used univariate monitoring tools. , However, process monitoring using the univariate tools is arduous for operations with a large number of variables due to the use of a dedicated control chart for each variable.…”

Section: Introductionmentioning

confidence: 99%

Robust Process Monitoring Methodology for Detection and Diagnosis of Unobservable Faults

Amin

Khan

Imtiaz

et al. 2019

Ind. Eng. Chem. Res.

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This paper presents a new integrated methodology for fault detection and diagnosis. The methodology is built using the multivariate exponentially weighted moving average principal component analysis (MEWMA-PCA) and the Bayesian network (BN) model. The fault detection is carried out using the MEWMA-PCA; diagnosis is completed utilizing the BN models. A novel supervisory learning-based methodology has been proposed to develop the BNs from the historical fault symptoms. Although the algorithm has been extensively applied to the Tennessee Eastman (TE) chemical process, monitoring of three specific (difficult to observe) faults, IDV 3, IDV 9, and IDV 15, has been demonstrated in this article. Most of the existing data-based methods have faced the challenge to detect these faults with a good detection rate (DR). Hence, these faults have been reported as either unobservable or strenuous to detect. Overall, fault detection performance of the squared prediction error (SPE) statistics combined with the MEWMA-PCA was found to be better than the T 2-based monitoring model. Although the cumulative sum (CUSUM) PCA-based approaches have demonstrated successful detection and diagnosis to these specific faults, the comparative studies suggest that the proposed methodology can outperform the CUSUM PCA approach.

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“…The next step is fault visualization that allocates data in two clusters by discriminant analysis and the last step is fault diagnosis. Pei et al [29] applied DA to detect discriminant direction, then used X/MR control chart to monitor a chemical process. Bazdar and Kazemzadeh [30] introduced discriminant function to distinguish the source of variation in multistage processes.…”

Section: Introductionmentioning

confidence: 99%

Monitoring multivariate-attribute quality characteristics in two stage processes using discriminant analysis based control charts

Zolfaghari

Amiri

2016

Scientia Iranica

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Abstract. In this paper, we focus speci cally on a two-stage process with multivariateattribute quality characteristics in the second stage. The main purpose of this study is extending Discriminant Analysis (DA) based control charts to monitor a two-stage process. We propose three methods including EWMA (DA), integrated EWMA (DA), and P-value (DA), and integrated Multivariate Exponentially Weighted Moving Average (MEWMA) and T 2 charts based on the DA approach to monitor the multivariate-attribute quality characteristic in a two-stage process. The performance of the proposed methods is evaluated through simulation studies as well as a real case.

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Fault Detection in Chemical Processes Using Discriminant Analysis and Control Chart

Cited by 13 publications

References 33 publications

JCEJ Outstanding Paper Award of 2008

JCEJ Outstanding Paper Award of 2008

Robust Process Monitoring Methodology for Detection and Diagnosis of Unobservable Faults

Monitoring multivariate-attribute quality characteristics in two stage processes using discriminant analysis based control charts

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