Near-field imaging based on an electromagnetic sensor has been widely used for nondestructive detection. An approach to detect the near-surface defects in pipeline coatings and dielectric pipelines is proposed. Based on the characteristics of resonant frequency shifts, a novel method using artificial neural network (ANN) is established to quantitatively evaluate circular-section shape defects in pipes, such as air bubbles in pipeline coating layers or qualitative characterize non-circular section-shape defects. The proposed method has three important modules: a new resonator for data acquisition, a signal-processing algorithm for data preprocessing, and an ANN for quantitative imaging. In the designed sensor, we extend the tip of the sensing ring and introduce an appending in the ring gap for high sensitivity. Simulations show that the sensor can detect a defect with a radius as small as 0.7 mm. The raw resonant frequency shifts obtained by the sensor scanning at an angle interv...

中文翻译：

---

微波技术和人工神经网络对管道进行无损评估

基于电磁传感器的近场成像已被广泛用于无损检测。提出了一种检测管道涂层和介质管道中近表面缺陷的方法。根据共振频率偏移的特征，建立了一种使用人工神经网络（ANN）的新方法来定量评估管道的圆形截面形状缺陷，例如管道涂层中的气泡或定性表征非圆形截面形状缺陷。所提出的方法具有三个重要模块：用于数据采集的新型谐振器，用于数据预处理的信号处理算法以及用于定量成像的ANN。在设计的传感器中，我们扩展了感应环的末端，并在环间隙中引入了一个附件，以实现高灵敏度。仿真表明，该传感器可以检测到半径仅为0.7毫米的缺陷。通过传感器以一定角度间隔扫描而获得的原始谐振频率偏移...