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A Stochastic Approach to Dynamic System Modeling With Application to Gas Turbines

[+] Author Affiliations
Hanny Bassily, John Wagner

Clemson University

Paper No. IMECE2005-79385, pp. 1069-1078; 10 pages
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Dynamic Systems and Control, Parts A and B
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 0-7918-4216-9 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME


Dynamic system mathematical models are a cornerstone for model based fault diagnostic techniques. However, achieving a robust mathematical model, based only on analytical derivations, cannot always be a realistic task especially for dynamic systems possessing physical parameter uncertainties and modeling errors. One attractive alternative is the huge capacity of stochastic methods to model non-deterministic highly complicated dynamic systems. In this paper, a general sensor-based modeling strategy is presented using stochastic techniques with application to a stationary gas turbine. The turbine sensors are monitored and recorded in real time to implement a signal modeling technique based on time series principles and turbine operation states. A subsequent recognition strategy, based on the turbine operation states and the Markov chain approach, is implemented to estimate the different signals. To experimentally validate the developed technique, input and output sensor data for a 4.5 MW stationary gas turbine has been recorded and analyzed during the critical start up phase. The proposed modeling strategy offers a considerable capability to recreate an unobserved signal from the observation of the other signals.

Copyright © 2005 by ASME



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