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Proceedings Paper

Fuzzy sensor fusion for gas turbine power plants
Author(s): Kai Goebel; Alice M. Agogino
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Paper 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.

Paper Details

Date Published: 12 March 1999
PDF: 10 pages
Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (12 March 1999); doi: 10.1117/12.341370
Show Author Affiliations
Kai Goebel, General Electric Co. (United States)
Alice M. Agogino, Univ. of California/Berkeley (United States)

Published in SPIE Proceedings Vol. 3719:
Sensor Fusion: Architectures, Algorithms, and Applications III
Belur V. Dasarathy, Editor(s)

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