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Frequency Selection for Reflectometry-based Soft Fault Detection using Principal Component Analysis

Abstract : This paper introduces an efficient approach to select the best frequency for soft fault detection in wired networks. In the literature, reflectometry method has been well investigated to deal with the problem of soft fault diagnosis (i.e. chafing, bending radius, pinching, etc.). Soft faults are characterized by a small impedance variation resulting in a low amplitude signature on the corresponding reflectograms. Accordingly, the detection of those faults depends strongly on the test signal frequency. Although the increase of test signal frequency enhances the soft fault "spatial" resolution, it provides signal attenuation and dispersion in electrical wired networks. In this context, the proposed method combines reflectometry-based data and Principal Component Analysis (PCA) algorithm to overcome this problem. To do so, the Time Domain Reflectometry (TDR) responses of 3D based-models of faulty coaxial cable RG316 and shielding damages have been simulated at different frequencies. Based on the obtained reflectograms, a PCA model is developed and used to detect the existing soft faults. This latter permits to determine the best frequency of the test signal to fit the target soft fault.
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Submitted on : Thursday, March 12, 2020 - 6:10:08 PM
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Nour Taki, Wafa Ben Hassen, Nicolas Ravot, Claude Delpha, Demba Diallo. Frequency Selection for Reflectometry-based Soft Fault Detection using Principal Component Analysis. Prognostics and Systems Health Management Conference (PHM-Paris 2019), May 2019, Paris, France. ⟨hal-02295328⟩



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