Statistical estimation of multiple faults in aircraft gas turbine engines

S. Sarkar, C. Rao, A. Ray

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This article presents estimation of multiple faults in aircraft gas-turbine engines, based on a statistical pattern recognition tool called symbolic dynamic filtering. The underlying concept is formulated by statistical analysis of evidences to estimate anomalies (i.e. deviations from the nominal values) in multiple critical parameters of the engine system; it also presents a framework for sensor information fusion. The fault estimation algorithm is validated on a numerical simulation test-bed that is built upon the NASA C-MAPSS model of a generic commercial aircraft engine.

Original languageEnglish (US)
Pages (from-to)415-424
Number of pages10
JournalProceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
Volume223
Issue number4
DOIs
StatePublished - Jun 1 2009

Fingerprint

Gas turbines
Turbines
Aircraft
Aircraft engines
Information fusion
Pattern recognition
NASA
Statistical methods
Engines
Sensors
Computer simulation

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Mechanical Engineering

Cite this

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Statistical estimation of multiple faults in aircraft gas turbine engines. / Sarkar, S.; Rao, C.; Ray, A.

In: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 223, No. 4, 01.06.2009, p. 415-424.

Research output: Contribution to journalArticle

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