System Decomposition for Distributed Multivariate Statistical Process Monitoring by Performance Driven Agglomerative Clustering

Shaaz Khatib, Prodromos Daoutidis, Ali Almansoori

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Conventional multivariate statistical process monitoring methods such as Principal Component Analysis perform poorly in detecting faults in large systems. Partitioning the system and implementing a multivariate statistical process monitoring method in a distributed manner improves monitoring performance. A simulation optimization method is proposed whose objective is to find the system decomposition for which the performance of a distributed multivariate statistical process monitoring method is optimal. The proposed method uses the search strategy used in agglomerative clustering in finding the optimal system decomposition. To demonstrate its effectiveness, the proposed method is incorporated into a distributed principal component analysis based monitoring scheme and applied to the benchmark Tennessee Eastman Process case study.

Original languageEnglish (US)
Pages (from-to)8283-8298
Number of pages16
JournalIndustrial and Engineering Chemistry Research
Volume57
Issue number24
DOIs
StatePublished - Jun 20 2018

Bibliographical note

Funding Information:
Financial support from the Petroleum Institute, Abu Dhabi and the National Science Foundation - CBET is gratefully acknowledged.

Publisher Copyright:
© 2018 American Chemical Society.

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