Performance improvements of wire fault diagnosis approach based on time-domain reflectometry

The aim of this study is to improve the performance of a wire fault diagnosis approach based on time-domain reflectometry (TDR). In this approach, the TDR response collected from measurements for a given (faulty) coaxial cable network is used as a reference for the cable network model response computed by an accurate analytical transmission line (ATL) method. The ATL model is iteratively tuned according to the outcome of the optimisation algorithm proposed. The advantage of the proposed ATL method over the more classic finite difference time-domain method is the extremely faster computational time which represents a significant factor for the diagnosis of faults in wiring networks. The proposed ATL method accurately provides the features of the resistance-inductance-conductance-capacitance-based model of a RG-58 coaxial cable employed as a device under test. The ATL model has been validated by comparison with measurement based and circuit simulations. Furthermore, seven experiments have been investigated in order to evaluate the performance of the ATL method for the diagnosis of wiring networks. The obtained results reveal that the ATL method together with the efficient optimisation algorithm is a reliable, efficient and fast method for the diagnosis of wiring networks.

[1]  S. A. Schelkunoff,et al.  The electromagnetic theory of coaxial transmission lines and cylindrical shields , 1934 .

[2]  Martin A. Green,et al.  A statistical method for evaluating electrical failures , 1994 .

[3]  Sailing He,et al.  Time-domain signal restoration and parameter reconstruction on an LCRG transmission line , 1995, Proceedings of ISSE'95 - International Symposium on Signals, Systems and Electronics.

[4]  T. Sarkar,et al.  Wideband frequency-domain characterization of FR-4 and time-domain causality , 2001, IEEE Transactions on Electromagnetic Compatibility.

[5]  Christer Svensson,et al.  Time domain modeling of lossy interconnects , 2001, ECTC 2001.

[6]  C. Aston,et al.  Biological warfare canaries [biological attack detection] , 2001, IEEE Spectrum.

[7]  Cynthia Furse,et al.  Frequency-domain reflectometry for on-board testing of aging aircraft wiring , 2003 .

[8]  Jin Bae Park,et al.  Application of time-frequency domain reflectometry for detection and localization of a fault on a coaxial cable , 2005, IEEE Transactions on Instrumentation and Measurement.

[9]  C. Furse,et al.  Analysis of spread spectrum time domain reflectometry for wire fault location , 2005, IEEE Sensors Journal.

[10]  Cynthia Furse,et al.  A critical comparison of reflectometry methods for location of wiring faults , 2006 .

[11]  Nicolas Ravot,et al.  A Simple and Accurate Model for Wire Diagnosis Using Re∞ectometry , 2007 .

[12]  Lionel Pichon,et al.  Detection of Defects in Wiring Networks Using Time Domain Reflectometry , 2010, IEEE Transactions on Magnetics.

[13]  Shang Chieh Wu,et al.  An iterative inversion method for transmission line fault location , 2011 .

[14]  J. Drewniak,et al.  From Maxwell Garnett to Debye Model for Electromagnetic Simulation of Composite Dielectrics Part I: Random Spherical Inclusions , 2011, IEEE Transactions on Electromagnetic Compatibility.

[15]  Ruhul A. Sarker,et al.  GA with a new multi-parent crossover for constrained optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[16]  Roger A. Dougal,et al.  Health Monitoring of Power Cable via Joint Time-Frequency Domain Reflectometry , 2011, IEEE Transactions on Instrumentation and Measurement.

[17]  Hashem M. Hashemian,et al.  State-of-the-Art Predictive Maintenance Techniques* , 2011, IEEE Transactions on Instrumentation and Measurement.

[18]  J. Drewniak,et al.  Homogenized Permittivity of Composites with Aligned Cylindrical Inclusions for Causal Electromagnetic Simulations , 2012 .

[19]  C. Furse,et al.  Advanced Forward Methods for Complex Wire Fault Modeling , 2013, IEEE Sensors Journal.

[20]  H. R. E. H. Bouchekara,et al.  Diagnosis of Multi-Fault Wiring Network Using Time-Domain Reflectometry and Electromagnetism-Like Mechanism , 2013 .

[21]  Ruhul A. Sarker,et al.  A new genetic algorithm for solving optimization problems , 2014, Eng. Appl. Artif. Intell..

[22]  H. R. E. H. Bouchekara,et al.  Diagnosis of wiring networks using Particle Swarm Optimization and Genetic Algorithms , 2014, Comput. Electr. Eng..

[23]  H. R. E. H. Bouchekara,et al.  Diagnosis of Multiple Wiring Faults Using Time-Domain Reflectometry and Teaching–Learning-Based Optimization , 2015 .

[24]  Antonio Orlandi,et al.  Wire fault diagnosis using time-domain reflectometry and backtracking search optimization algorithm , 2015, 2015 31st International Review of Progress in Applied Computational Electromagnetics (ACES).

[25]  G. Bucci,et al.  Exploring Remote Monitoring of Degraded Compression and Bolted Joints in HV Power Transmission Lines , 2016, IEEE Transactions on Power Delivery.

[26]  Antonio Orlandi,et al.  Detectability of Degraded Joint Discontinuities in HV Power Lines Through TDR-Like Remote Monitoring , 2016, IEEE Transactions on Instrumentation and Measurement.

[27]  Lionel Pichon,et al.  Non-destructive diagnosis of wiring networks using time domain reflectometry and an improved black hole algorithm , 2017 .

[28]  H. R. E. H. Bouchekara,et al.  Optimal power flow using GA with a new multi-parent crossover considering: prohibited zones, valve-point effect, multi-fuels and emission , 2018 .