Measurement-based Condition Monitoring of Railway Signaling Cables

TL;DR

This paper introduces a comprehensive, measurement-based approach for monitoring railway signaling cables, combining fault detection, location, and health assessment to improve underground cable maintenance.

Contribution

It presents a novel composite diagnostics framework integrating orthogonal multitone reflectometry and Bayesian health indices for railway cable monitoring.

Findings

Effective fault detection and location demonstrated with real-world data

Bayesian health index accurately indicates cable degradation levels

Measurement campaign validates the proposed diagnostic approach

Abstract

We propose a composite diagnostics solution for railway infrastructure monitoring. In particular, we address the issue of soft-fault detection in underground railway cables. We first demonstrate the feasibility of an orthogonal multitone time domain reflectometry based fault detection and location method for railway cabling infrastructure by implementing it using software defined radios. Our practical implementation, comprehensive measurement campaign, and our measurement results guide the design of our overall composite solution. With several diagnostics solutions available in the literature, our conglomerated method presents a technique to consolidate results from multiple diagnostics methods to provide an accurate assessment of underground cable health. We present a Bayesian framework based cable health index computation technique that indicates the extent of degradation that a cable is subject to at any stage during its lifespan. We present the performance results of our proposed solution using real-world measurements to demonstrate its effectiveness.