基于自旋霍尔磁阻传感器和机器学习的金属裂纹Eddy Current检测
Eddy Current Testing of Metal Cracks Using Spin Hall Magnetoresistance Sensor and Machine Learning
摘要：
最近，我们已经开发出一种自旋霍尔磁阻（SMR）传感器，工作在交流偏置和感测电流。在这里，我们从理论上和实验上证明，SMR传感器是唯一适合涡流检测应用，因为线圈和传感器都利用交流电流作为激励源。SMR传感器的使用有效地消除了用于检测涡流的任何解调或锁定技术的必要性，这极大地简化了检测系统。此外，我们表明，主成分分析和决策树模型的组合是有效的分类金属裂纹。SMR传感器获得的相对干净的信号大大方便了后续的信号分析，并确保了不同类型裂纹特征分类的高精度。
英文摘要：
Recently we have developed a spin Hall magnetoresistance (SMR) sensor which operates under AC bias and sense currents. Here we demonstrate both theoretically and experimentally that the SMR sensor is uniquely suited for eddy current testing applications because both the coil and sensor utilize AC current as the excitation source. The use of SMR sensor effectively eliminates the necessity of any demodulation or lock-in technique for detecting the eddy current, which greatly simplifies the detection system. Furthermore, we show that the combination of principal component analysis and decision tree model is effective in classifying the metal cracks. The relatively clean signals obtained by the SMR sensor greatly facilitates the subsequent signal analysis and ensures high accuracy in the classification of different types of crack features.
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