Dynamic-Inner Canonical Correlation Analysis based Process Monitoring

Yining DONG, S.Joe QIN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

6 Citations (Scopus)

Abstract

Recently, a dynamic-inner canonical correlation analysis (DiCCA) algorithm has been developed for high dimensional dynamic data modeling, which provides separate and explicit modeling of dynamic components and static components in the data. In this paper, a DiCCA based process monitoring algorithm is proposed. Compared to existing statistical process monitoring algorithms, the proposed algorithm has three advantages. First, the proposed algorithm is able to monitor dynamic variations and static variations separately. Second, the predictions of the dynamic components from the past values are utilized to shrink the uncertainty in process monitoring, leading to reduced type II errors without increasing type I errors. Third, once a faulty sample is detected, the contribution of this sample is not further utilized for fault detection in future samples, mitigating fault propagation issues. Case studies on simulation datasets and the Tennessee Eastman dataset demonstrate the effectiveness of the proposed method.
Original languageEnglish
Title of host publication2020 American Control Conference (ACC)
PublisherInstitute of Electrical and Electronics Engineers
Pages3553-3558
Number of pages6
ISBN (Electronic)9781538682661
ISBN (Print)9781538682678
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: 1 Jul 20203 Jul 2020

Publication series

NameProceedings of the American Control Conference
PublisherInstitute of Electrical and Electronics Engineers
Volume2020-July
ISSN (Print)0743-1619
ISSN (Electronic)2378-5861

Conference

Conference2020 American Control Conference, ACC 2020
Country/TerritoryUnited States
CityDenver
Period1/07/203/07/20

Fingerprint

Dive into the research topics of 'Dynamic-Inner Canonical Correlation Analysis based Process Monitoring'. Together they form a unique fingerprint.

Cite this