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Use of Sparse Principal Component Analysis (SPCA) for Fault Detection
University of California, Davis.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Department of Geology, University of California, Davis.
Number of Authors: 3
2016 (English)In: IFAC PAPERSONLINE, ISSN 2405-8963, Vol. 49, no 7, 693-698 p.Article in journal (Refereed)
Abstract [en]

Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the original variables which can be numerous in most modern applications. To address this challenge, we first propose the use of sparse principal component analysis (SPCA) where the loadings of some variables in principal components are restricted to zero. This paper then describes a technique to determine the number of non-zero loadings in each principal component. Furthermore, we compare the performance of PCA and SPCA in fault detection. The validity and potential of SPCA are demonstrated through simulated data and a comparative study with the benchmark Tennessee Eastman process

Place, publisher, year, edition, pages
2016. Vol. 49, no 7, 693-698 p.
Keyword [en]
Information technology - Automatic control
Keyword [sv]
Informationsteknik - Reglerteknik
National Category
Reliability and Maintenance
Research subject
Quality Technology and Management
URN: urn:nbn:se:ltu:diva-35445DOI: 10.1016/j.ifacol.2016.07.259ISI: 000381504800117ScopusID: 2-s2.0-84991038654Local ID: 9fc24481-0603-440c-94d6-8e1de51f646bOAI: diva2:1008698
IFAC Symposium on Dynamics and Control of Process SystemsIncluding Biosystems : 06/06/2016 - 08/06/2016

Validerad; 2016; Nivå 1; 2016-10-07 (andbra); Konferensartikel i tidskrift

; Konferensartikel i tidskriftAvailable from: 2016-09-30 Created: 2016-09-30 Last updated: 2016-12-07Bibliographically approved

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Kulahci, Murat
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ReferencesLink to record
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