Identification of Failed (Fissured) Fuel Rods in Nuclear Reactors Using Neural Processing and Principal Component Analysis

C. C. B. TelesJosé Manoel de Seixas

A possible way to detect failed (fissured) rods, within a nuclear fuel assembly, is sounding the rods with ultrasonic pulses and examining the received echo waveforms. The detection is performed by a multilayer feedforward neural classifier, trained according to the backpropagation algorithm. The classifier achieved a detection efficiency of 93% (for failed rods) with 3% as false-alarm probability. Data compaction through principal component analysis reduced the network?s input vector to 1.5% of its original length, with no efficiency loss.

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