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Signal Processing Schemes for Defect Identification and Sizing from Signals of Eddy Current Testing in Nuclear Power Plant

机译:核电站涡流检测信号识别缺陷和确定尺寸的信号处理方案

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In this paper, signal processing schemes developed for upgrading the Eddy Current Testing (ECT) system utilized for In Service Inspection (ISI) of Steam Generator (SG) tubes of a nuclear power plant are presented. Schemes for the wobbling noise recognition, signal indication extraction, signal classification, and the separation of mixed signals are proposed. The schemes are applied to both signals measured in a SG mock-up and signals from short tube test-pieces measured in a laboratory environment. It is demonstrated that the methods work well even for mixed signals in the support plate region of SG tubes. As it is possible to adjust the position of a signal point based on the signature of the support plates, noise signals from a welding line or a bending zone can be recognized based on the information of design and manufactory. The mixed signals of defects and support structures are processed by using a similarity analysis strategy and the identification of defect signals is performed by means of both a neural network approach and a statistic method. Satisfactory results are obtained for the processing of measured signals.
机译:在本文中,提出了为升级用于核电站蒸汽发生器(SG)管的在役检查(ISI)的涡流测试(ECT)系统而开发的信号处理方案。提出了抖动噪声识别,信号指示提取,信号分类和混合信号分离的方案。该方案既适用于在SG模型中测量的信号,也适用于在实验室环境中测量的短管测试件的信号。证明了该方法即使对于SG管的支撑板区域中的混合信号也有效。由于可以基于支撑板的特征来调整信号点的位置,因此可以基于设计和制造信息来识别来自焊接线或弯曲区域的噪声信号。缺陷和支撑结构的混合信号通过相似性分析策略进行处理,缺陷信号的识别通过神经网络方法和统计方法进行。对于测量信号的处理,获得了令人满意的结果。

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