Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process

Simple item page
dc.contributor.authorCao, Yi
dc.contributor.authorSamuel, Raphael
dc.date.accessioned2016-07-08T15:17:14Z
dc.date.available2016-07-08T15:17:14Z
dc.date.issued2016-05-19
dc.description.abstractDynamic principal component analysis (DPCA) is commonly used for monitoring multivariate processes that evolve in time. However, it is has been argued in the literature that, in a linear dynamic system, DPCA does not extract cross correlation explicitly. It does not also give the minimum dimension of dynamic factors with non zero singular values. These limitations reduces its process monitoring effectiveness. A new approach based on the concept of dynamic latent variables is therefore proposed in this paper for extracting latent variables that exhibit dynamic correlations. In this approach, canonical variate analysis (CVA) is used to capture process dynamics instead of the DPCA. Tests on the Tennessee Eastman challenge process confirms the workability of the proposed approach.en_UK
dc.identifier.citationCao, Y., Samuel, R. (2016) Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process, IEEE International Conference on Industrial Technology, ICIT 2016, Taipei, Taiwan, 14-17 March 2016en_UK
dc.identifier.urihttp://dx.doi.org/10.1109/ICIT.2016.7474861
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/10111
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.titleDynamic latent variable modelling and fault detection of Tennessee Eastman challenge processen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
fault_detection_of_Tennessee_Eastman_challenge_process-2016.pdf.pdf
Size:
442.57 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.79 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SWEE)