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Closed-loop identification of the data-driven SKR with deterministic disturbance for fault detection

  • Harbin Institute of Technology
  • China Academy of Engineering Physics

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Industrial systems are always subjected to the deterministic disturbance due to some inherited factors, which is likely to degrade the control and monitoring performance to some extent. This paper presents an approach to the closed-loop subspace identification of the data-driven stable kernel representation (SKR) with the deterministic disturbance. The essence is that we extend the CSIMPCA algorithm by introducing the deterministic disturbance and subsequently separate the part corresponding to the SKR of the system from the obtained parity space. The inspiration for the idea mainly stems from the necessity for the identification and process monitoring of practical closed-loop systems. The effectiveness of the proposed method is demonstrated and illustrated through randomly generated 4-order MIMO discrete-time LTI systems. Furthermore, the identified SKR is finally applied to the fault detection and related experimental results show a decent detection performance.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5365-5370
Number of pages6
ISBN (Electronic)9781509066841
DOIs
StatePublished - 26 Dec 2018
Event44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 - Washington, United States
Duration: 20 Oct 201823 Oct 2018

Publication series

NameProceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

Conference

Conference44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018
Country/TerritoryUnited States
CityWashington
Period20/10/1823/10/18

Keywords

  • CSIMPCA
  • Closed-loop subspace identification
  • Data-driven SKR
  • Deterministic disturbance
  • Fault detection

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