Symbolic transient time-series analysis for fault detection in aircraft gas turbine engines

Soumalya Sarkar, Kushal Mukherjee, Soumik Sarkar, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

This paper presents a data-driven symbolic dynamics-based method for detection of incipient faults in gas turbine engines of commercial aircraft. Detection of incipient faults in such engines could be significantly manifested by taking advantage of transient data (e.g., during takeoff). From this perspective, the fault detection and classification algorithms are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of steady state data is extended by taking advantage of the available transient data. The fault detection and classification procedure has been validated on the NASA C-MAPSS transient test case generator.

Original languageEnglish (US)
Title of host publication2012 American Control Conference, ACC 2012
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5132-5137
Number of pages6
ISBN (Print)9781457710957
DOIs
StatePublished - 2012
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: Jun 27 2012Jun 29 2012

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2012 American Control Conference, ACC 2012
CountryCanada
CityMontreal, QC
Period6/27/126/29/12

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Symbolic transient time-series analysis for fault detection in aircraft gas turbine engines'. Together they form a unique fingerprint.

Cite this