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Proceedings Article

Fuzzy sensor fusion for gas turbine power plants

[+] Author Affiliations
Kai Goebel

General Electric Co. (USA)

Alice M. Agogino

Univ. of California/Berkeley (USA)

Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, 52 (March 12, 1999); doi:10.1117/12.341370
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From Conference Volume 3719

  • Sensor Fusion: Architectures, Algorithms, and Applications III
  • Belur V. Dasarathy
  • Orlando, FL | April 05, 1999

abstract

In this paper we present a methodology for fuzzy sensor fusion. We then apply this methodology to sensor data from a gas turbine power plant. The developed fusion algorithm tackles several problems: 1) It aggregates redundant sensor information; this allows making decision which sensors should be considered for propagation of sensor information. 2) It filters out noise and sensor failure from measurements; this allows a system to operate despite temporary or permanent failure of one or more sensors. For the fusion, we use a combination of direct and functional redundancy. The fusion algorithm uses confidence values obtained for each sensor reading form validation curves and performs a weighted average fusion. With increasing distance from the predicted value, readings are discounted through a non-linear validation function. They are assigned a confidence value accordingly. The predicted value in the described algorithm is obtained through application of a fuzzy exponential weighted moving average time series predictor with adaptive coefficients. Experiments on real data from a gas turbine power plant show the robustness of the fusion algorithm which leads to smooth controller input values.

© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Kai Goebel and Alice M. Agogino
"Fuzzy sensor fusion for gas turbine power plants", Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, 52 (March 12, 1999); doi:10.1117/12.341370; http://dx.doi.org/10.1117/12.341370


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