Anomaly detection for health management of aircraft gas turbine engines

Devendra Tolani, Murat Yasar, Shin Chin, Asok Ray

Research output: Contribution to journalConference articlepeer-review

30 Scopus citations


This paper presents a comparison of different pattern recognition algorithms to identify slow time scale anomalies for health management of aircraft gas turbine engines. A new tool of anomaly detection, based on Symbolic Dynamics and Information Theory, is compared with traditional pattern recognition tools of Principal Component Analysis (PCA) and Artificial Neural Network (ANN). Time series data of the observed variables on the fast time scale are analyzed at slow time scale epochs for early detection of anomalies. The time series data are obtained from a generic engine simulation model. Health monitoring of gas turbine engines based on these techniques is discussed.

Original languageEnglish (US)
Article numberWeA15.1
Pages (from-to)459-464
Number of pages6
JournalProceedings of the American Control Conference
StatePublished - 2005
Event2005 American Control Conference, ACC - Portland, OR, United States
Duration: Jun 8 2005Jun 10 2005

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


Dive into the research topics of 'Anomaly detection for health management of aircraft gas turbine engines'. Together they form a unique fingerprint.

Cite this