Wire mismatch detection using a convolutional neural network and fault localization based on time-frequency-domain reflectometry

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.