Joint Parametric Fault Diagnosis and State Estimation Using KF-ML Method

Zhen Sun, Zhenyu Yang

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

4 Citations (Scopus)
463 Downloads (Pure)

Abstract

The paper proposes a new method for a kind of parametric fault online diagnosis with state estimation jointly. The considered fault affects not only the deterministic part of the system but also the random circumstance. The proposed method first applies Kalman Filter (KF) and Maximum Likelihood (ML) technique to identify the fault parameter and employs the result to make fault decision based on the predefined threshold. Then this estimated fault parameter value is substituted into parameterized state estimation of KF to obtain the state estimation. Finally, a robot case study with two different fault scenarios shows this method can lead to a good performance in terms of fast and accurate fault detection and state estimation.
Original languageEnglish
Title of host publicationProceedings of the 19th World Congress of the International Federation of Automatic Control, IFAC 2014
Number of pages6
PublisherIFAC Publisher
Publication date2014
Pages8293-8298
DOIs
Publication statusPublished - 2014
Event19th World Congress of the International Federation of Automatic Control, IFAC 2014 - Cape Town, South Africa
Duration: 24 Aug 201429 Aug 2014

Conference

Conference19th World Congress of the International Federation of Automatic Control, IFAC 2014
Country/TerritorySouth Africa
CityCape Town
Period24/08/201429/08/2014
SeriesI F A C Workshop Series
ISSN1474-6670

Keywords

  • Parameter Identification
  • State Estimation
  • Kalman Filter
  • Maximum Likelihood

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