Diagnosis of Shielded Cable Faults via Regression-Based Reflectometry

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 enviro...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2019-03, Vol.66 (3), p.2122-2131
Main Authors: Kwon, Gu-Young, Lee, Chun-Kwon, Shin, Yong-June
Format: Article
Language:English
Subjects:
Cable shielding
Delay effects
Economic impact
Electric cables
Electric equipment
Fault diagnosis
Fault location
Feature extraction
general regression neural network (GRNN)
General regression neural networks
History
Industrial equipment
insulation defects
Neural networks
Power cables
Reflectance
Reflectometry
shielded cable
Stability
Time-frequency analysis
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Summary: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.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2018.2840529