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

Seung Jin Chang, Jin Bae Park

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8360944
Pages (from-to)2102-2110
Number of pages9
JournalIEEE Transactions on Industrial Electronics
Volume66
Issue number3
DOIs
Publication statusPublished - 2019 Mar

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Wire mismatch detection using a convolutional neural network and fault localization based on time-frequency-domain reflectometry'. Together they form a unique fingerprint.

  • Cite this