Frequency Scanning System by Multi-frequency Excitation Spectrogram Method

本课题的目的之一是提出一种新的用于涡流多频系统仿真和缺陷识别的神经网络模型。在本项目中，采用多频激励和频谱涡流系统(MFES)和逆神经模型来检测和识别蒸汽发生器管中的天然缺陷。结果表明，该传感器的动态神经模型具有很高的运算速度和识别结果的可靠性。其次，研究了磁场中巴克豪森噪声的混沌行为。该噪声是由磁畴的突然去钉扎引起的磁化跳跃产生的。我们研究了噪声在无损检测技术中的应用。结果表明，巴克豪森噪声可以在旋转磁场中连续产生，该信号具有与测量材料中的检测或流动有关的信息的可能性在范围内。我们通过嵌入的方法从重构的吸引子中澄清巴克豪森噪声中混沌的存在。然后，我们研制了用于该系统的便携式仪器。

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.