Characterization and Anomaly Reasoning in Signaling & Power Cables

Abstract

<p>Power and signaling infrastructure comprise of electrical installations, electronic systems and cables. A damage to these cables will be catastrophic or at the least significant downtime to the system. Hence, monitoring these cables in real-time not only improves the efficiency of the system but can also avoid fatal accidents. In this work, we develop a non-invasive composite diagnostic framework to identify cable damages such as insulation cuts. The framework can detect, classify, and locate faults. While the solution is general enough, we consider two use cases: (a) railway cables used in signalling applications and(b) symmetrical four-core cables used in residential buildings. In order to characterize the faults, we use three non-invasive sensors: (a) SNR sensor, (b) S-parameter, and (c) Correlation peak sensor. We use a single programmable hardware to implement each of these sensors. These sensors monitor a parameter change on the cable in real-time. The experimental insights gained are used to construct an a priori Bayesian network depicting the non-deterministic relationship between an effect and its causes. This uncertainty is due to inhibitors such as sensor calibration issues and coupling mismatch between sensor transceivers suitably handled through a Noisy-OR function. The results of Bayesian inference with belief propagation provides up to 97% match with the ground truth state. Regarding the cable fault, our experimental results show a best-case detection and localization accuracy of 98% & 97.2% respectively.</p>