Least Squares Sparse Principal Component Analysis and Parallel Coordinates for Real-Time Process Monitoring-Reference-Cited by-同舟云学术

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Least Squares Sparse Principal Component Analysis and Parallel Coordinates for Real-Time Process Monitoring

Published:2020-08-09 Issue:35 Volume:59 Page:15656-15670
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Gajjar Shriram¹,Kulahci Murat²³,Palazoglu Ahmet¹^ORCID

Affiliation:

1. Department of Chemical Engineering, University of California, Davis, Davis, California 95616, United States

2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby 2800, Denmark

3. Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, Luleå 971 87, Sweden

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.0c01749

Reference71 articles.

1. A review of process fault detection and diagnosis

2. Survey on data-driven industrial process monitoring and diagnosis

3. Russell, E. L.; Chiang, L. H.; Braatz, R. D. Data-Driven Methods for Fault Detection and Diagnosis in Chemical Processes; Springer-Verlag London, 2000; XIII, p 192.

4. Chemical Process Performance Evaluation

5. The process chemometrics approach to process monitoring and fault detection

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