Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

D. Lieftucht; Uwe Kruger; Lei Xie; Timothy Littler; Q. Chen; S.Q. Wang

doi:10.1021/ie060017b

Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

D. Lieftucht, Uwe Kruger, Lei Xie, Timothy Littler, Q. Chen, S.Q. Wang

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.

Original language	English
Pages (from-to)	1677-1688
Number of pages	12
Journal	Industrial and Engineering Chemistry Research
Volume	45 (5)
Issue number	5
DOIs	https://doi.org/10.1021/ie060017b
Publication status	Published - 01 Feb 2006

ASJC Scopus subject areas

Chemical Engineering (miscellaneous)
General Environmental Science
Polymers and Plastics

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10.1021/ie060017b

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AU - Xie, Lei

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AU - Chen, Q.

AU - Wang, S.Q.

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N2 - This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.

AB - This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.

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JO - Industrial and Engineering Chemistry Research

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ER -

Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

Abstract

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