Semantic sensor fusion for fault diagnosis in aircraft gas turbine engines

Soumik Sarkar, Dheeraj Sharan Singh, Abhishek Srivastav, Asok Ray

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

3 Scopus citations

Abstract

Data-driven fault diagnosis of a complex system such as an aircraft gas turbine engine requires interpretation of multi-sensor information to assure enhanced performance. This paper proposes feature-level sensor information fusion in the framework of symbolic dynamic filtering. This hierarchical approach involves construction of composite patterns consisting of: (i) atomic patterns extracted from single sensor data and (ii) relational patterns that represent the cross-dependencies among different sensor data. The underlying theories are presented along with necessary assumptions and the proposed method is validated on the NASA C-MAPSS simulation model of aircraft gas turbine engines.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages220-225
Number of pages6
ISBN (Print)9781457700804
DOIs
StatePublished - Jan 1 2011

Publication series

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

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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  • Cite this

    Sarkar, S., Singh, D. S., Srivastav, A., & Ray, A. (2011). Semantic sensor fusion for fault diagnosis in aircraft gas turbine engines. In Proceedings of the 2011 American Control Conference, ACC 2011 (pp. 220-225). [5991168] (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/acc.2011.5991168