Frequency Scanning System by Multi-frequency Excitation Spectrogram Method

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One purpose of this project is to present new neural network model used for eddy current multi-frequency system simulation and flaws identification. In this project a Multi-Frequency Excitation and Spectrogram Eddy Current System (MFES) and an inverse neural model were used to detect and identify natural flaws in steam generator tubes. It is shown that the applied dynamic neural model of the ECT sensor offers very high speed of operation and guarantees reliability of the recognition results.Next, we present chaotic behavior of the Barkhausen noise generated in magnetic fields. This noise is generated by the magnetization jumps caused by abrupt depinning of magnetic domains. We have studied the application of the noise to nondestructive testing techniques. The results showed that the Barkhausen noise could be continuously generated in a rotational magnetic field and it was within the bounds of possibility that the signal had information referent to detects or flows in measuring materials. We investigated to make the existence of Chaos in the Barkhausen noise clear from the reconstructed attractor by Embedding.Then we developed the portable apparatus for this system.