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 por...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2019-03, Vol.66 (3), p.2102-2110
Main Authors: Chang, Seung Jin, Park, Jin Bae
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Attenuation
Bandwidth
Compensation
Convolutional neural network (CNN)
Convolutional neural networks
Cross correlation
Design optimization
Electric wire
Fault location
Filter banks
Frequency domain analysis
Image acquisition
Impedance
Machine learning
Neural networks
Reflectometry
Time-frequency analysis
Wavelet transforms
Wire
wire mismatch
Wires
Wiring
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Summary: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.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2018.2835386