| 摘要 |
<p>Loose parts are sensed moving in a conduit carrying a flowing material, such as the cooling circuit of a pressurized water nuclear reactor. An acoustic pickup produces an electrical signal with vibration of the conduit due to impact of the loose part, and background noise. A signal processor encodes the values of distinct parameters of the electrical signal such as amplitude, amplitude at particular frequencies, etc., in an ongoing manner, producing discrete output values. These outputs are coupled as inputs to a neural network with physical or logical neuron cells loaded with weighting factors affecting the strength and polarity of neural interconnections. The factors represent the acoustic signature of the loose part. Products of the input values and the weighting factors are summed to produce one or more neural network outputs, compared to a threshold. The sum normally varies randomly, but has a strong swing when the pattern is encountered, due to the factors emphasizing the pattern over background noise. The threshold comparison operates a display or alarm. The weighting factors are learned by repeating empirical tests and correlating the factors to the signal to minimize error.</p> |
