DOI: 10.1016/s0967-0661(99)00040-4. PCA-SDG based process monitoring and fault diagnosis

PCA-SDG based process monitoring and fault diagnosis

10.1016/s0967-0661(99)00040-4

Crossref journal-article

Elsevier BV

Control Engineering Practice (78)

Bibliography

Vedam, H., & Venkatasubramanian, V. (1999). PCA-SDG based process monitoring and fault diagnosis. Control Engineering Practice, 7(7), 903â917.

Authors 2

Hiranmayee Vedam (first)
Venkat Venkatasubramanian (additional)

References 26 Referenced 135

10.1002/aic.690421011 / AIChE Journal / Identification of faulty sensors using principal component analysis by Dunia (1996)
10.1016/0003-2670(86)80028-9 / Analytica Chimica Acta / Partial least squares regression by Geladi (1986)
10.1016/0098-1354(79)80079-4 / Computers and Chemical Engineering / An algorithm for diagnosis of system failures in chemical processes by Iri (1979)
10.2307/1267757 / Technometrics / Control procedures for residuals associated with principal component analysis by Jackson (1979)
10.1016/0004-3702(87)90063-4 / Artificial Intelligence / Diagnosing multiple faults by Kleer (1987)
10.1002/aic.690330703 / AIChE Journal / Rule-based approach to fault diagnosis using the signed directed graph by Kramer (1987)
10.1002/cjce.5450690105 / Canadian Journal of Chemical Engineering / Multivariate statistical monitoring of process operating performance by Kresta (1991)
10.1016/0005-1098(94)90109-0 / Automatica / Model based causal reasoning for process supervision by Leyval (1994)
10.1002/aic.690400509 / AIChE Journal / Process monitoring and diagnosis by multiblock PLS methods by MacGregor (1994)
10.1002/aic.690280519 / AIChE Journal / Detection of gross errors in process data by Mah (1982)
10.1016/0098-1354(93)80021-E / Computers and Chemical Engineering / Dynamic simulator for a model IV fluid catalytic cracking unit by McFarlane (1993)
{'key': '10.1016/S0967-0661(99)00040-4_BIB20', 'series-title': 'Artificial intelligence in process engineering', 'author': 'Morales', 'year': '1990'} / Artificial intelligence in process engineering by Morales (1990)
Mylaraswamy, D. (1996). DKIT: A blackboard-based, distributed, multi-expert environment for Abnormal Situation Management. Ph.D. Thesis. Purdue University.
Mylaraswamy, D., Kavuri, S. N., & Venkatasubramanian, V. (1994). A framework for automated development of causal models for fault diagnosis. AIChE Annual Meeting, San Francisco.
{'issue': '9', 'key': '10.1016/S0967-0661(99)00040-4_BIB2', 'first-page': '36', 'article-title': 'Adequately address abnormal situation operations', 'volume': '91', 'author': 'Nimmo', 'year': '1995', 'journal-title': 'Chemical Engineering Progress'} / Chemical Engineering Progress / Adequately address abnormal situation operations by Nimmo (1995)
10.1002/aic.690400809 / AIChE Journal / Monitoring batch processes using multiway principal component analysis by Nomikos (1994)
10.1002/aic.690340906 / AIChE Journal / Qualitative simulation of chemical process systems by Oyeleye (1988)
Rengaswamy, R. (1995). A framework for integrating process monitoring, diagnosis and supervisory control. Ph.D. Thesis. Purdue University.
10.1021/ie00044a021 / Industrial Engineering and Chemistry Research / Use of fuzzy cause-effect digraph for resolution fault diagnosis for process plants– I. Fuzzy cause-effect digraph by Shin (1995)
{'issue': '4', 'key': '10.1016/S0967-0661(99)00040-4_BIB17', 'first-page': '651', 'article-title': 'Fault diagnosis of chemical processes by the use of signed digraph extension to five-range pattern of abnormality', 'volume': '25', 'author': 'Shiozaki', 'year': '1985', 'journal-title': 'International Chemical Engineering'} / International Chemical Engineering / Fault diagnosis of chemical processes by the use of signed digraph extension to five-range pattern of abnormality by Shiozaki (1985)
10.1016/0098-1354(96)00000-2 / Computers and Chemical Engineering / Detecting persistent gross errors by sequential analysis of principal component analysis by Tong (1996)
10.1016/S0098-1354(97)87577-1 / Computers and Chemical Engineering / Signed digraph based multiple fault diagnosis by Vedam (1997)
Venkatasubramanian, V., Kavuri, S. N., & Rengaswamy, R. (1995). Process fault diagnosis – an Overview. CIPAC Technical Report, Purdue University.
10.1016/0098-1354(94)80131-2 / Methodology. Computers and Chemical Engineering / The possible cause-effect graph model for process fault diagnosis – I by Wilcox (1994)
Wise, B. M., & Gallagher, N. B. (1996). PLS Toolbox: for Use with MATLAB. Eigenvector Technologies.
10.1016/0169-7439(87)80084-9 / Chemometrics and Intelligent Laboratory Systems / Principal component analysis by Wold (1987)

Dates

Type	When
Created	23 years ago (July 25, 2002, 9:26 a.m.)
Deposited	6 years, 3 months ago (April 24, 2019, 3:28 p.m.)
Indexed	1 month ago (July 19, 2025, 11:53 p.m.)
Issued	26 years, 1 month ago (July 1, 1999)
Published	26 years, 1 month ago (July 1, 1999)
Published Print	26 years, 1 month ago (July 1, 1999)

Funders 0

None

BibTeX

@article{Vedam_1999, title={PCA-SDG based process monitoring and fault diagnosis}, volume={7}, ISSN={0967-0661}, url={http://dx.doi.org/10.1016/s0967-0661(99)00040-4}, DOI={10.1016/s0967-0661(99)00040-4}, number={7}, journal={Control Engineering Practice}, publisher={Elsevier BV}, author={Vedam, Hiranmayee and Venkatasubramanian, Venkat}, year={1999}, month=jul, pages={903–917} }

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