Owing to the increasing complexity of electrical systems, diagnostic techniques of cables used for connecting electrical elements are essential for system maintenance in order to prevent a failure that can cause significant impacts on the overall electrical systems. Multicore structures are typically used as control and instrumentation cables in nuclear power plants, and the failure of the control and instrumentation cables can result in a disaster such as a radiation leak. In this paper, a method for the diagnosis of multicore cables is proposed based on the reflectometry. The diagnosis relates to the classification of defective cores in joint, which is one of the weakest parts in cable systems. The reflected signals obtained through reflectometry are converted into images by an advanced image processing algorithm, and the images are classified using artificial neural networks. The proposed method is demonstrated by experimental data using a real-world multicore cable. In the experiment, the faults are emulated similar to real-world defects using a potentiometer. It is expected that the proposed technique will enhance the stability and reliability of multicore cable systems.
Bibliographical noteFunding Information:
Manuscript received February 13, 2019; revised April 9, 2019; accepted May 17, 2019. Date of publication June 7, 2019; date of current version January 3, 2020. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning, #NRF-2017R1A2A1A05001022, and in part by the National Research Foundation of Korea through the framework of the International Cooperation Program under Grant 2016K2A9A1A03905116. (Corresponding author: Yong-June Shin.) S. S. Bang and Y.-J. Shin are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:, firstname.lastname@example.org).
© 1982-2012 IEEE.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering