Soft fault diagnosis in wiring networks using reflectometry and Principal Component Analysis

Abstract

During operation, cables may be subject to hard faults (open circuit, short circuit) or soft faults (isolation damage, pinching, etc.) due to misuse, environmental conditions, or aging. Even though several electric and non-electric wire diagnosis methods have been studied and developed throughout the last few decades, reflectometry-based techniques have provided effective results with hard faults. However, they have shown to be less effective for soft faults. Indeed, soft faults are characterized by a small impedance variation, resulting in a low amplitude signature in the reflectograms. Accordingly, the detection of these faults depends strongly on the test signal bandwidth. Although the increase of the maximal frequency of the test signal enhances the soft fault’s ”spatial” resolution, the performance is limited by signal attenuation and dispersion. This study proposes a method to select the best maximal frequency for soft fault detection. It is based on a combination of reflectometry with Principal Component Analysis (PCA), and the analysis of the Squared Prediction Error (SPE). Experimental validation is carried out, and performance analysis in the presence of noise is investigated. The results for shielding damage show that when the soft fault is near the injection point, the detection probability equals to one even for SNR values as low as 0 dB. As the fault position approaches the end of the cable, the performance is still acceptable, but for lower fault severities, the detection is almost impossible. The results also show that the selected frequency depends on the fault severity, the fault position, and the noise level.