A Hybrid Model for Fault Diagnosis Using Model Based Approaches and Support Vector Machine-Reference-Cited by-同舟云学术

A Hybrid Model for Fault Diagnosis Using Model Based Approaches and Support Vector Machine

Published:2006 Issue:10 Volume:39 Page:1085-1095
ISSN:0021-9592
Container-title:JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
language:en
Short-container-title:J. Chem. Eng. Japan

Author:

Lee Chang Jun¹,Lee Gibaek²,Han Chonghun¹,Yoon and En Sup¹

Affiliation:

1. School of Chemical and Biological Engineering, Seoul National University

2. Department of Chemical and Biological Engineering, Chungju National University

Publisher

Society of Chemical Engineers, Japan

Subject

General Chemical Engineering,General Chemistry

Link

http://www.jstage.jst.go.jp/article/jcej/39/10/39_10_1085/_pdf

Reference22 articles.

1. Becraft, W. R., D. Z. Guo, P. L. Lee and R. B. Newel; “Fault Diagnosis Strategies for Chemical Plants: A Review of Competing Technologies,” Proceedings of Process Systems Eng., ’91, Vol. 2, pp. 12.1–12.15, Quebec, Canada (1991)

2. Cherkassky, V. and F. Muler; Learning from Data: Concepts, Theory, and Methods, pp. 353–387, John Wiley & Sons, New York, U.S.A. (1998)

3. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

4. Chiang, L. H., E. L. Russell and R. D. Braatz; Fault Detection and Diagnosis in Industrial Systems, pp. 103–169, Springer-Verlag, London, U.K. (2001)

5. Fault diagnosis based on Fisher discriminant analysis and support vector machines

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