In this article, we propose a fault detection and assessment technique for instrumentation and control cables based on time-frequency image classification using the faster region-based convolutional neural network (R-CNN). To train the faster R-CNN while compensating for multiple reflections, the reflected signal estimation is utilized, which divides the reflected signal into the signal propagation along the cable and the reflection from the impedance discontinuity point. Experimental results on two fault scenarios under the circumstance of multiple faults detection and branched networks demonstrate the effectiveness of the proposed method.
|Number of pages||10|
|Journal||IEEE Transactions on Industrial Electronics|
|Publication status||Published - 2021 Feb|
Bibliographical noteFunding Information:
Manuscript received May 19, 2019; revised September 15, 2019, November 10, 2019, and January 1, 2020; accepted January 12, 2020. Date of publication February 5, 2020; date of current version October 30, 2020. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, Information & Communication Technology (ICT) & Future Planning under Grant NRF-2020R1A2B5B03001692. (Corresponding author: Yong-June Shin.) C.-K. Lee is with the Korea Electric Power Corporation Research Institute, Daejeon 24056, South Korea (e-mail: email@example.com).
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering