Multivariate Statistical Process Monitoring Scheme with PLS and SVDD

Jia Liu and Yan-guang Sun
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Jia Liu: Automation Research and Design Institute of Metallurgical Industry
Yan-guang Sun: Automation Research and Design Institute of Metallurgical Industry

A chapter in Proceedings of 20th International Conference on Industrial Engineering and Engineering Management, 2013, pp 57-70 from Springer

Abstract: Abstract In order to adaptably monitor product qualities during real industrial process, a new multivariate statistical process monitoring scheme combining projection to latent spaces (PLS) and Support Vector Domain Description (SVDD) is proposed. PLS can establish the monitoring space, which maximizes the correlation between process variables and quality variables and enable product qualities monitoring through process variables. SVDD can define the admissible domain by normal operation data without constraints about data distribution. Moreover, with kernel functions it can even provide a tight admissible domain for the operation data. Such characteristics make it suitable for practical production processes. This scheme is then applied to Tennessee Eastman process, and its efficiency for fault detection is proved by introducing simulated process faults. Analysis about its limits in fault detection is also presented.

Keywords: Kernel functions; Multivariate statistical process monitoring; Projection to latent spaces (PLS); Support vector domain description (SVDD); Tennessee Eastman process (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-40072-8_6

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DOI: 10.1007/978-3-642-40072-8_6

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