Fault diagnosis based on set membership identification using output-error models

V. Reppa, Antonios Tzes

Research output: Contribution to journalArticle

Abstract

The objective of this article is the design of a fault diagnosis method based on set membership identification for systems subject to abrupt parametric faults. The proposed method assumes an output-error, linearly parametrizable model, with unknown but bounded noise corrupting the measurement data. The set membership identification's objective is to compute (a) the smallest volume-wise ellipsoid that contains the nominal parameter vector, which concurrently resides within a data-hypersector and (b) the monotonically nonincreasing parametric set arisen from the orthotopes that tightly outer bound the ellipsoids. The fault detection mechanism is based on consistency tests between the estimated ellipsoids and the data-hypersectors and the intersection of support orthotopes. At the sample instant of the fault detection, set-theoretic operations are applied for the subsequent fault isolation and identification. The fault isolation is based on the projections of the intersections of orthotopes, while the distance of their centers is used for fault identification. Simulations studies are used to verify the efficiency of the suggested method applied on an electrostatic microactuator subject to several abrupt failure modes.

Original languageEnglish (US)
Pages (from-to)224-255
Number of pages32
JournalInternational Journal of Adaptive Control and Signal Processing
Volume30
Issue number2
DOIs
StatePublished - Feb 1 2016

Fingerprint

Fault detection
Failure analysis
Identification (control systems)
Microactuators
Failure modes
Electrostatics

Keywords

  • electrostatic microactuator
  • fault detection
  • fault identification
  • fault isolation
  • set membership identification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Fault diagnosis based on set membership identification using output-error models. / Reppa, V.; Tzes, Antonios.

In: International Journal of Adaptive Control and Signal Processing, Vol. 30, No. 2, 01.02.2016, p. 224-255.

Research output: Contribution to journalArticle

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