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Detection of rotating stall based on deterministic learning

  • Wenjie Si
  • , Cong Wang
  • , Binhe Wen
  • , Yong Wang
  • , Anping Hou

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

Abstract

In this paper, deterministic learning theory is used to detect the stall inception signal for the axial compressor. Firstly, based on deterministic learning (DL) theory, the system dynamics underlying normal and stall inception signal are identified and stored in constant radial basis function (RBF) networks. Secondly, through the method of dynamic pattern recognition in DL, the stall inception of the axial compressor could be detected. Simulation results show the validity of the proposed approach.

Original languageEnglish
Title of host publicationProceedings of the 32nd Chinese Control Conference, CCC 2013
PublisherIEEE Computer Society
Pages3332-3337
Number of pages6
ISBN (Print)9789881563835
StatePublished - 18 Oct 2013
Event32nd Chinese Control Conference, CCC 2013 - Xi'an, China
Duration: 26 Jul 201328 Jul 2013

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference32nd Chinese Control Conference, CCC 2013
Country/TerritoryChina
CityXi'an
Period26/07/1328/07/13

Keywords

  • Deterministic learning
  • Dynamic pattern recognition
  • Identification
  • Rapid detection
  • Rotating stall

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