发表机构
University of Koblenz（科布伦茨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种晶体管数少于100的全模拟超声损伤检测系统，利用模拟希尔伯特变换和神经网络实现传感器内计算，在钢板上验证了可行性。
AI 中文摘要
超声检测（UT）常用于检测结构（如金属板）中的损伤。传感器通过例如PZT换能器获取超声波信号。时间分辨的传感器信号必须用模拟电子器件处理，例如放大和滤波。通常随后使用模数转换器进行数字化，最终利用强大的微处理器系统处理数字传感器信号，应用数字信号处理、特征提取和机器学习。数字处理系统的缺点是晶体管数量多（微芯片面积大）、能耗高、处理过程依赖状态，因此对供电中断敏感。在硅基电子之外，印刷有机电子器件日益受到关注。但印刷电子仍局限于低晶体管和电子元件数量（通常为100个）。我们将研究并展示一个全模拟信号处理和特征提取系统，该系统由推导信号包络的模拟希尔伯特变换、用于特征提取的简单模拟算术运算以及最终使用模拟人工神经网络进行损伤分类和回归组成。我们期望一个晶体管数少于100的完整损伤检测系统。我们将使用带有圆形缺陷的钢板上的PZT换能器信号来测试我们的损伤检测系统。本工作的重点是信号包络的模拟计算（使用全通滤波器网络近似希尔伯特变换）、模拟特征提取以及损伤预测，形成一个无需数字计算机即可进行传感器内计算的模拟计算机。
英文摘要
Ultrasonic Testing (UT) is commonly used to detect damage in structures, e.g., metal plates. A sensor acquires Ultrasonic waves, e.g., by using PZT transducers. The time-resolved sensor signal must be processed with analog electronics, e.g., amplified and filtered. Commonly a digitalization follows using an Analog-to-Digital converter, finally processing the digital sensor signal, applying digital signal processing, feature extraction, and Machine Learning by using powerful microprocessor systems. The disadvantages of digital processing systems are their high number of transistors (microchip area), energy consumption, state-dependent processing and therefore sensitivity to energy supply interruption. Beyond silicon electronics, printed organic electronics gains interest. But printed electronics is still limited to low transistor and electronic component counts (typically 100). We will investigate and demonstrate a fully analog signal processing and feature extraction system consisting of an analog Hilbert transform deriving the signal envelope, simple analog arithmetic calculations for feature extraction, and finally damage classification and regression using an analog Artificial Neural Network. We expect a full damage detection system with less than 100 transistors. We will test our damage detection system with PZT transducer signals from Steel plates with circular defects. The focus of this work is the analog computation of the signal envelope (using all-pass filter networks for approximation of the Hilbert transform) and the analog feature extraction as well as the prediction of damage, forming an analog computer which can perform in-sensor computation, computing without a digital computer.
CommentsNDTonline, International Online Conference on Nondestructive Testing 2026
