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提出了一种多线圈涡流无损检测方法,通过相空间模糊熵算法分析涡流信号复杂度,进而实现对金属微小缺陷形状的辨识。为了从足够的测量信息中获取有效的缺陷特征,设计了多线圈传感器模型。通过仿真实验选取适合的传感器参数和激励模式。采用相空间模糊熵算法,研究不同大小、深度、形状的缺陷对涡流信号复杂度的影响。为了准确提取涡流信号的内在规律,获得对缺陷敏感的信号分析结果,对涡流信号进行相空间重构,并在重构的相空间中计算信号的模糊熵。分析结果表明:随着缺陷体积的增加,模糊熵增大,涡流信号的复杂度增加。根据不同形状缺陷的模糊熵均值分布图,可以实现对孔、洞、裂缝3种缺陷较精确的区分。
A multi-coil eddy current non-destructive testing method is proposed. The phase space fuzzy entropy algorithm is used to analyze the complexity of the eddy current signal, and then the shape of the metal micro-defect is identified. In order to obtain effective defect characteristics from sufficient measurement information, a multi-coil sensor model is designed. Through the simulation experiment, select the appropriate sensor parameters and excitation mode. The phase space fuzzy entropy algorithm is used to study the influence of different size, depth and shape defects on the eddy current signal complexity. In order to accurately extract the inherent law of eddy current signals, obtain signal analysis results sensitive to defects, reconstruct the phase space of the eddy current signals and calculate the entropy of signals in the reconstructed phase space. The analysis results show that with the increase of defect volume, the fuzzy entropy increases, and the complexity of eddy current signal increases. According to the distribution map of fuzzy entropy of different shape defects, it is possible to achieve a more accurate distinction between the three defects of holes, holes and cracks.