Multivariable Process Monitoring using Nonlinear Approaches

Ricardo Dunia H.; S. Joe Qin; Thomas F. Edgar

doi:10.1109/acc.1995.529352

Multivariable Process Monitoring using Nonlinear Approaches

Ricardo Dunia H.
, S. Joe Qin
, Thomas F. Edgar

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

5 Citations (Scopus)

Abstract

The use of Principal component analysis (PCA) for process monitoring applications has attracted much attention recently. The idea of compressing the process data into a few factors facilitates and simplifies the identification of an abnormal operation condition. Nonlinear factors obtained by the implementation of neural nets enhance this reduction specially in processes with broad operation conditions. This paper summarizes and compares the techniques used to obtain nonlinear factors. It also discusses the advantages of using nonlinear PCA for monitoring and calculation of confidence regions.

Original language	English
Title of host publication	Proceedings of 1995 American Control Conference - ACC'95
Publisher	IEEE
Pages	756-760
ISBN (Print)	0-7803-2445-5
DOIs	https://doi.org/10.1109/acc.1995.529352
Publication status	Published - Jun 1995
Externally published	Yes
Event	1995 American Control Conference - Seattle, WA, USA Duration: 21 Jun 1995 → 23 Jun 1995 https://ieeexplore.ieee.org/document/529766

Publication series

Name	Proceedings of the American Control Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISSN (Print)	0743-1619

Conference

Conference	1995 American Control Conference
City	Seattle, WA, USA
Period	21/06/95 → 23/06/95
Internet address	https://ieeexplore.ieee.org/document/529766

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10.1109/acc.1995.529352

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title = "Multivariable Process Monitoring using Nonlinear Approaches",

abstract = "The use of Principal component analysis (PCA) for process monitoring applications has attracted much attention recently. The idea of compressing the process data into a few factors facilitates and simplifies the identification of an abnormal operation condition. Nonlinear factors obtained by the implementation of neural nets enhance this reduction specially in processes with broad operation conditions. This paper summarizes and compares the techniques used to obtain nonlinear factors. It also discusses the advantages of using nonlinear PCA for monitoring and calculation of confidence regions.",

author = "\{Dunia H.\}, Ricardo and Qin, \{S. Joe\} and Edgar, \{Thomas F.\}",

year = "1995",

month = jun,

doi = "10.1109/acc.1995.529352",

language = "English",

isbn = "0-7803-2445-5",

series = "Proceedings of the American Control Conference",

publisher = "IEEE",

pages = "756--760",

booktitle = "Proceedings of 1995 American Control Conference - ACC'95",

address = "United States",

note = "1995 American Control Conference ; Conference date: 21-06-1995 Through 23-06-1995",

url = "https://ieeexplore.ieee.org/document/529766",

}

Multivariable Process Monitoring using Nonlinear Approaches. / Dunia H., Ricardo; Qin, S. Joe; Edgar, Thomas F.
Proceedings of 1995 American Control Conference - ACC'95. IEEE, 1995. p. 756-760 (Proceedings of the American Control Conference).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Multivariable Process Monitoring using Nonlinear Approaches

AU - Dunia H., Ricardo

AU - Qin, S. Joe

AU - Edgar, Thomas F.

PY - 1995/6

Y1 - 1995/6

N2 - The use of Principal component analysis (PCA) for process monitoring applications has attracted much attention recently. The idea of compressing the process data into a few factors facilitates and simplifies the identification of an abnormal operation condition. Nonlinear factors obtained by the implementation of neural nets enhance this reduction specially in processes with broad operation conditions. This paper summarizes and compares the techniques used to obtain nonlinear factors. It also discusses the advantages of using nonlinear PCA for monitoring and calculation of confidence regions.

AB - The use of Principal component analysis (PCA) for process monitoring applications has attracted much attention recently. The idea of compressing the process data into a few factors facilitates and simplifies the identification of an abnormal operation condition. Nonlinear factors obtained by the implementation of neural nets enhance this reduction specially in processes with broad operation conditions. This paper summarizes and compares the techniques used to obtain nonlinear factors. It also discusses the advantages of using nonlinear PCA for monitoring and calculation of confidence regions.

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0029191043&origin=recordpage

U2 - 10.1109/acc.1995.529352

DO - 10.1109/acc.1995.529352

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0-7803-2445-5

T3 - Proceedings of the American Control Conference

SP - 756

EP - 760

BT - Proceedings of 1995 American Control Conference - ACC'95

PB - IEEE

T2 - 1995 American Control Conference

Y2 - 21 June 1995 through 23 June 1995

ER -

Multivariable Process Monitoring using Nonlinear Approaches

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