Symbolic identification for anomaly detection in aircraft gas turbine engines

Subhadeep Chakraborty, Soumik Sarka, Asok Ray, Shashi Phoha

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

2 Scopus citations

Abstract

This paper presents a robust and computationally inexpensive technique of fault detection in aircraft gas-turbine engines, based on a recently developed statistical pattern recognition tool. The method involves abstraction of a qualitative description from a general dynamical system structure, using state space embedding of the output data-stream and discretization of the resultant pseudo state and input spaces. The system identification is achieved through grammatical inference techniques, and the deviation of the plant output from the nominal estimated language gives a metric for fault detection. The 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)
Title of host publicationProceedings of the 2010 American Control Conference, ACC 2010
Pages5954-5959
Number of pages6
Publication statusPublished - Oct 15 2010
Event2010 American Control Conference, ACC 2010 - Baltimore, MD, United States
Duration: Jun 30 2010Jul 2 2010

Publication series

NameProceedings of the 2010 American Control Conference, ACC 2010

Other

Other2010 American Control Conference, ACC 2010
CountryUnited States
CityBaltimore, MD
Period6/30/107/2/10

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Cite this

Chakraborty, S., Sarka, S., Ray, A., & Phoha, S. (2010). Symbolic identification for anomaly detection in aircraft gas turbine engines. In Proceedings of the 2010 American Control Conference, ACC 2010 (pp. 5954-5959). [5531246] (Proceedings of the 2010 American Control Conference, ACC 2010).