Wiring networks diagnosis using time‐domain reflectometry and support vector machines-Reference-Cited by-同舟云学术

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Wiring networks diagnosis using time‐domain reflectometry and support vector machines

Published:2020-03 Issue:2 Volume:14 Page:220-224
ISSN:1751-8830
Container-title:IET Science, Measurement & Technology
language:en
Short-container-title:IET Science, Measurement & Technology

Author:

Kamel Smail Mostafa¹,Boubezoul Abderrahmane²,Bouchekara Houssem R.E.H.³^ORCID,Sellami Yamine¹

Affiliation:

1. Institut Polytechnique des Sciences Avancées63 boulevard de brandebourg94200Ivry‐sur‐SeineFrance

2. UPE‐IFSTTAR/TS2/SIMU&MOTOF‐77447 Marne la Vallée CedexFrance

3. Department of Electrical EngineeringUniversity of Hafr Al BatinHafr Al Batin31991KSA

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-smt.2019.0122

Reference19 articles.

1. A critical comparison of reflectometry methods for location of wiring faults

2. Frequency‐domain reflectometry for on‐board testing of aging aircraft wiring;Furce C.;IEEE Trans. Electron. Compat.,2003

3. Analysis of spread spectrum time domain reflectometry for wire fault location

4. Capacitance and inductance sensor circuits for detecting the lengths of open‐ and short‐circuited wires;Chung Y.Ch.;IEEE Trans. Instr. Meas.,2009

5. Detection of the defects in wiring networks using time domain reflectometry;Smail M.K.;IEEE Trans. Magn.,2010

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1. Nondestructive testing of chips faults based on Terahertz pulse time-domain reflection technology;Physics Letters A;2024-11

2. Enhanced complex wire fault diagnosis via integration of time domain reflectometry and particle swarm optimization with least square support vector machine;IET Science, Measurement & Technology;2024-03-07

3. Wiring networks diagnosis using K-Nearest neighbour classifier and dynamic time warping;Nondestructive Testing and Evaluation;2024-01-14

4. Enhanced artificial intelligence technique for soft fault localization and identification in complex aircraft microgrids;Engineering Applications of Artificial Intelligence;2024-01

5. A novel deep convolutional neural network and its application to fault diagnosis of the squirrel-cage asynchronous motor under noisy environment;Measurement Science and Technology;2023-08-02

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