Simplification techniques for EKF computations in fault diagnosis: Model decomposition

Chuei Tin Chang, Jung Ing Hwang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

The extended Kalman filter (EKF) is one of the most popular model-based techniques for fault detection and diagnosis. In this study, the suboptimal EKF technique is utilized to enhance computation efficiency without sacrificing diagnostic accuracy. In particular, three simple strategies are proposed to decompose the filter model according to the precedence order of the state/parameter estimation process. The computation load needed in fault identification can be reduced significantly by implementing all or part of these decomposed EKFs on-line. Extensive simulation results are also presented to demonstrate the effectiveness of these proposed techniques.

Original languageEnglish
Pages (from-to)1392-1402
Number of pages11
JournalAIChE Journal
Volume44
Issue number6
Publication statusPublished - 1998 Dec 1

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Environmental Engineering
  • Chemical Engineering(all)

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