In addition to diagnosing a wiring of the vehicle in operation, it is also very important to find wire mismatches during the assembly process. In this paper, we propose a new method combining time–frequency-domain reflectometry and deep learning to verify that the wire is connected to the proper port of the underhood electrical center. Considering the frequency characteristics of each wire (black, blue, red, and yellow), we develop an optimization signal design algorithm. Using the time–frequency cross correlation (TFCC), the reflected signal generated at the impedance discontinuities is acquired and converted into the Wigner–Ville distribution image. Through the proposed algorithm, the existing images are converted into new images, which are easy to distinguish among groups. The new images are used as input of the convolutional neural network and trained to learn the feature of each group. The lengths, compensation filters, and the port information to be connected to each wire are stored in the filter bank. If the distance derived using the TFCC is different from the stored length, the wire is considered defective, and the acquired signal is restored by the compensation filter designed by the overcomplete wavelet transform method. Experimental results demonstrate the effectiveness of the proposed method for detecting the wire mismatch and fault location.

中文翻译：

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使用卷积神经网络的导线不匹配检测和基于时频域反射法的故障定位

除了诊断运行中的车辆的线路外，在装配过程中发现线路不匹配也非常重要。在本文中，我们提出了一种结合时频域反射计和深度学习的新方法来验证电线是否连接到发动机罩下电气中心的正确端口。考虑到每根导线（黑色、蓝色、红色和黄色）的频率特性，我们开发了一种优化信号设计算法。使用时频互相关 (TFCC)，在阻抗不连续处生成的反射信号被采集并转换为 Wigner-Ville 分布图像。通过提出的算法，将现有图像转换为新图像，易于区分组。新图像被用作卷积神经网络的输入，并被训练以学习每组的特征。长度、补偿滤波器和要连接到每条电线的端口信息都存储在滤波器组中。如果使用 TFCC 得出的距离与存储的长度不同，则认为导线有缺陷，并且通过过完备小波变换方法设计的补偿滤波器恢复获取的信号。实验结果证明了所提出的方法在检测导线不匹配和故障定位方面的有效性。认为导线有缺陷，通过过完备小波变换方法设计的补偿滤波器对采集的信号进行恢复。实验结果证明了所提出的方法在检测导线不匹配和故障定位方面的有效性。认为导线有缺陷，通过过完备小波变换方法设计的补偿滤波器对采集的信号进行恢复。实验结果证明了所提出的方法在检测导线不匹配和故障定位方面的有效性。