Abstract
It is well known that the performance of fuzzy neural networks applied to sensor monitoring strongly depends on the selection of inputs. In their applications to sensor monitoring, there are usually a large number of input variables related to a relevant output. As the number of input variables increases, the required training time of a fuzzy neural network increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and moreover, to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this chapter, an automatic input selection routine was described which combines the principal component analysis, correlation analysis, and genetic algorithm to select important input features. Also, whether the sensors fail or not is determined by applying the sequential probability ratio test to the residuals between the estimated signals and the measured signals. The described sensor failure detection method was verified through applications to the steam generator water level, the steam generator steam flowrate, the pressurizer water level, and the pressurizer pressure sensors in pressurized water reactors.
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Na, M.G., Sim, Y.R., Park, K.H., Upadhyaya, B.R., Lu, B., Zhao, K. (2002). Failure Detection Using a Fuzzy Neural Network with an Automatic Input Selection Algorithm. In: Ruan, D., Fantoni, P.F. (eds) Power Plant Surveillance and Diagnostics. Power Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04945-7_15
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DOI: https://doi.org/10.1007/978-3-662-04945-7_15
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