Soft fault detection in cables using the cluster time-frequency domain reflectometry

被引:21
|
作者
机构
[1] Université Paris Est, ESYCOM (EA 2552), CNAM
来源
| 1600年 / Institute of Electrical and Electronics Engineers Inc., United States卷 / 02期
关键词
Cables; Clustering methods; Crosstalk; Fault location; Multiconductor transmission lines; Reflectometry; Time Domain Reflectometry; Time-Frequency analysis;
D O I
10.1109/MEMC.2013.6512221
中图分类号
学科分类号
摘要
Faced with the continual increase in complexity of wiring networks, their reliability, in particular in embedded fields (such as automotive and aerospace industries) becomes a major issue. In wiring diagnostics, reflectometry methods are commonly used. They are quite efficient for detecting important damage (hard faults) such as short-or open-circuits. These faults can have heavy material (e.g.: fires) and economical consequences. Then detecting them before they happen would be very interesting and useful. Unfortunately no current method is efficient enough for addressing soft faults. This article proposes an original approach to overcome this problem: the Cluster Time-Frequency Domain Reflectometry (CTFDR). Based on the fact that a wire is most often in a bundle with several other wires, this method takes advantage of the resultant near end crosstalk signals to get more information about the state of the wires in the bundle. To make the detection of incipient faults easier, a normalized time-frequency cross correlation function has also been used. © 2013 IEEE Electromagnetic Compatibility Magazine.
引用
收藏
页码:54 / 69
页数:15
相关论文
共 50 条
  • [21] Multiple Soft Defect Signature Magnification in Electrical Cables With Binary Time-Domain Reflectometry
    Auzanneau, Fabrice
    IEEE SENSORS LETTERS, 2020, 4 (07)
  • [22] Application of Enhanced Optimal-Detection of Time-Frequency Domain Reflectometry on HTS Cable With High-Resolution
    Lim, Hobin
    Lee, Yeong Ho
    Bang, Su Sik
    Shin, Yong-June
    IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, 2023, 33 (05)
  • [23] Transmission line fault detection using time-frequency analysis
    Samantaray, SR
    Dash, PK
    Panda, G
    INDICON 2005 PROCEEDINGS, 2005, : 162 - 166
  • [24] Soft Fault Identification in Electrical Network Using Time Domain Reflectometry and Neural Network
    Laib, A.
    Melit, M.
    Nekhoul, B.
    Drissi, K. El Khamlichi
    Kerroum, K.
    ADVANCED CONTROL ENGINEERING METHODS IN ELECTRICAL ENGINEERING SYSTEMS, 2019, 522 : 365 - 376
  • [25] Advanced detection of multiple PD sources in cables using time-frequency transformations
    Sabarshad, Omid
    Akbari, Asghar
    ELECTRIC POWER SYSTEMS RESEARCH, 2025, 246
  • [26] Measurements of Characteristic Impedance of High Frequency Cables with Time Domain Reflectometry (TDR)
    Navarro, Luis
    Mayevskiy, Eugene
    Chairet, Timothy
    72ND ARFTG MICROWAVE MEASUREMENT SYMPOSIUM, 2008, : 9 - 19
  • [27] Wiring fault detection with Boolean-chaos time-domain reflectometry
    Jian Guo Zhang
    Hang Xu
    Bing Jie Wang
    Li Liu
    Peng Cheng Su
    Jing Xia Li
    Nonlinear Dynamics, 2015, 80 : 553 - 559
  • [28] Wiring fault detection with Boolean-chaos time-domain reflectometry
    Zhang, Jian Guo
    Xu, Hang
    Wang, Bing Jie
    Liu, Li
    Su, Peng Cheng
    Li, Jing Xia
    NONLINEAR DYNAMICS, 2015, 80 (1-2) : 553 - 559
  • [29] Wire Mismatch Detection Using a Convolutional Neural Network and Fault Localization Based on Time-Frequency-Domain Reflectometry
    Chang, Seung Jin
    Park, Jin Bae
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 66 (03) : 2102 - 2110
  • [30] Abnormality Monitoring for Three-Phase HTS Cable via Time-Frequency Domain Reflectometry
    Bang, Su Sik
    Shin, Yong-June
    IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, 2021, 31 (05)