Diagnosis of shielded cable faults via regression-based reflectometry

Gu Young Kwon, Chun Kwon Lee, Yong June Shin

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8370789
Pages (from-to)2122-2131
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume66
Issue number3
DOIs
Publication statusPublished - 2018 Jun 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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