Multi-directional reconstruction based contributions for root-cause diagnosis of dynamic processes

Gang LI, S. Joe QIN, Tianyou CHAI

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

24 Citations (Scopus)

Abstract

Dynamic principal component analysis (DPCA) is required for the modeling and monitoring of dynamic processes. However, the root cause identification of faulty variables is quite desired after a fault is detected. As DPCA based methods construct detection indices in augmented variable space, it is difficult to use contribution analysis for diagnosis in a common way. In recent literature, reconstruction based contribution (RBC) is proposed, which is more efficient to diagnose sensors responsible for a fault than traditional contribution analysis. However, they both suffer from smearing effect. In this paper, an extended method of RBC based on DPCA is proposed to select multiple faulty variables in the sense of reconstruction, which is called multi-directional RBC. The case study on continuous stirred tank reactor (CSTR) process is used to demonstrate the effectiveness of the proposed approach. © 2014 American Automatic Control Council.
Original languageEnglish
Title of host publication2014 American Control Conference (ACC)
PublisherInstitute of Electrical and Electronics Engineers
Pages3500-3505
Number of pages6
ISBN (Electronic)9781479932740
ISBN (Print)9781479932726
DOIs
Publication statusPublished - Jun 2014
Externally publishedYes
Event2014 American Control Conference, ACC 2014 - Portland, United States
Duration: 4 Jun 20146 Jun 2014

Publication series

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

Conference

Conference2014 American Control Conference, ACC 2014
Country/TerritoryUnited States
CityPortland
Period4/06/146/06/14

Keywords

  • dynamic principal component analysis
  • multi-directional reconstruction based contribution
  • Root cause diagnosis
  • smearing effect

Fingerprint

Dive into the research topics of 'Multi-directional reconstruction based contributions for root-cause diagnosis of dynamic processes'. Together they form a unique fingerprint.

Cite this