Multi-PCA models for process monitoring and fault diagnosis

Liling Ma; Yunbo Jiang; Fuli Wang; Furong Gao

Multi-PCA models for process monitoring and fault diagnosis

Liling Ma^*, Yunbo Jiang, Fuli Wang, Furong Gao

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

Multivariate statistical approaches have been proved effective for reducing the dimension of highly correlated process variables and subsequently simplifying the tasks of process monitoring and fault diagnosis. However, for the process with distinctive stages, a single statistical model is not sufficient or even incapable to map the substantive process information. In this paper, multi-PCA models are proposed for promptly detecting faults and improving the exactness of the diagnosis as well. The effectiveness of the approach is demonstrated on a complicated fermentation process.

Original language	English
Pages (from-to)	667-672
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	37
Issue number	1
Publication status	Published - 2004
Externally published	Yes
Event	7th International Symposium on Advanced Control of Chemical Processes, ADCHEM 2003 - , Hong Kong Duration: 11 Jan 2004 → 14 Jan 2004

Keywords

Clustering technology
Fault diagnosis
Multi-PCA models
Process monitoring

Cite this

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title = "Multi-PCA models for process monitoring and fault diagnosis",

abstract = "Multivariate statistical approaches have been proved effective for reducing the dimension of highly correlated process variables and subsequently simplifying the tasks of process monitoring and fault diagnosis. However, for the process with distinctive stages, a single statistical model is not sufficient or even incapable to map the substantive process information. In this paper, multi-PCA models are proposed for promptly detecting faults and improving the exactness of the diagnosis as well. The effectiveness of the approach is demonstrated on a complicated fermentation process.",

keywords = "Clustering technology, Fault diagnosis, Multi-PCA models, Process monitoring",

author = "Liling Ma and Yunbo Jiang and Fuli Wang and Furong Gao",

note = "Publisher Copyright: Copyright {\textcopyright} IFAC 2003; 7th International Symposium on Advanced Control of Chemical Processes, ADCHEM 2003 ; Conference date: 11-01-2004 Through 14-01-2004",

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AU - Ma, Liling

AU - Jiang, Yunbo

AU - Wang, Fuli

AU - Gao, Furong

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Y1 - 2004

N2 - Multivariate statistical approaches have been proved effective for reducing the dimension of highly correlated process variables and subsequently simplifying the tasks of process monitoring and fault diagnosis. However, for the process with distinctive stages, a single statistical model is not sufficient or even incapable to map the substantive process information. In this paper, multi-PCA models are proposed for promptly detecting faults and improving the exactness of the diagnosis as well. The effectiveness of the approach is demonstrated on a complicated fermentation process.

AB - Multivariate statistical approaches have been proved effective for reducing the dimension of highly correlated process variables and subsequently simplifying the tasks of process monitoring and fault diagnosis. However, for the process with distinctive stages, a single statistical model is not sufficient or even incapable to map the substantive process information. In this paper, multi-PCA models are proposed for promptly detecting faults and improving the exactness of the diagnosis as well. The effectiveness of the approach is demonstrated on a complicated fermentation process.

KW - Clustering technology

KW - Fault diagnosis

KW - Multi-PCA models

KW - Process monitoring

UR - http://www.scopus.com/inward/record.url?scp=85083252716&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85083252716

SN - 2405-8963

VL - 37

SP - 667

EP - 672

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 1

T2 - 7th International Symposium on Advanced Control of Chemical Processes, ADCHEM 2003

Y2 - 11 January 2004 through 14 January 2004

ER -

Multi-PCA models for process monitoring and fault diagnosis

Abstract

Keywords

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