Anomaly detection in aircraft gas turbine engines

Devendra Tolani, Murat Yasar, Asok Ray, Vigor Yang

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

The application of a novel method for early detection of anomalies in complex mechanical systems is discussed. The anomaly detection method is based on symbolic time series analysis (STSA) and its efficacy has been examined on the simulation test bed of a twin-engine propulsion system. the time series data of observed macroscopic variables, generated on the first time scale from the simulation model, are analyzed at slow time scale epochs for each detection of anomalies. the method is found to be more efficient compared to principal component analysis (PCA) and artificial neural network (ANN) methods.

Original languageEnglish (US)
Pages (from-to)44-51
Number of pages8
JournalJournal of Aerospace Computing, Information and Communication
Volume3
Issue number2
DOIs
StatePublished - Feb 1 2006

Fingerprint

Time series analysis
Principal component analysis
Propulsion
Gas turbines
Time series
Turbines
Aircraft
Engines
Neural networks

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Tolani, Devendra ; Yasar, Murat ; Ray, Asok ; Yang, Vigor. / Anomaly detection in aircraft gas turbine engines. In: Journal of Aerospace Computing, Information and Communication. 2006 ; Vol. 3, No. 2. pp. 44-51.
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Anomaly detection in aircraft gas turbine engines. / Tolani, Devendra; Yasar, Murat; Ray, Asok; Yang, Vigor.

In: Journal of Aerospace Computing, Information and Communication, Vol. 3, No. 2, 01.02.2006, p. 44-51.

Research output: Contribution to journalArticle

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