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Online Adaptive Model Based Fault Detection, Isolation and Estimation Method

[+] Author Affiliations
Aniket Gupta, Karolos Grigoriadis, Matthew Franchek

University of Houston, Houston, TX

Daniel J. Smith

Cummins, Columbus, IN

Paper No. DSCC2011-6080, pp. 929-936; 8 pages
doi:10.1115/DSCC2011-6080
From:
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 1
  • Arlington, Virginia, USA, October 31–November 2, 2011
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5475-4
  • Copyright © 2011 by ASME

abstract

In the present study a methodology to perform fault detection, isolation and estimation is proposed that is based on adaptive model based techniques. Fault detection and diagnostics is performed by comparing the coefficients of healthy system model with that of adapted online coefficients. This approach is shown to be robust to modeling errors, sensor noise and process variability. The proposed approach is applied to FTP-75 cycle simulation data of exhaust gas recircultaion (EGR) faults and is shown to effectively perform fault detection and diagnosis.

Copyright © 2011 by ASME
Topics: Flaw detection

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