Fault diagnosis has been studied actively across the electrical industry to help maintain the stability of electrical equipment. Among this equipment, shielded cables, which are widely used in various industrial sectors, require careful and periodic diagnosis, owing to their poor installation environments and potential for creating huge economic losses. Reflectometry is a representative solution to locate the cable faults. However, conventional reflectometry techniques require prior knowledge about the cable under test, such as the reference wave velocity, total length of the cable, etc. Moreover, the degree of failure cannot be determined using conventional methods. In this paper, a novel reflectometry technique is proposed to locate and evaluate the faults in a cable,without requiring any prior knowledge. A general regression neural network based on the kernel density estimation is utilized with special feature extraction procedures. The proposed method is tested in an actual test bed with two types of emulated faults, and is found to estimate both the fault location and reflection coefficient successfully. It is expected that the proposed method can improve the stability of industrial equipment.
|Number of pages||10|
|Journal||IEEE Transactions on Industrial Electronics|
|Publication status||Published - 2018 Jun 1|
Bibliographical noteFunding Information:
Manuscript received December 8, 2017; revised February 27, 2018 and April 6, 2018; accepted May 6, 2018. Date of publication June 1, 2018; date of current version October 31, 2018. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT and Future Planning, #NRF-2017R1A2A1A05001022, and was supported under the framework of international cooperation program managed by National Research Foundation of Korea (#2017K1A4A3013579). (Corresponding author: Yong-June Shin.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: yongjune@ yonsei.ac.kr).
© 2018 Institute of Electrical and Electronics Engineers Inc.All right reserved.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering