  Remote monitoring of electrical cable conditions is an essential
characteristic of the next-generation smart grid, which features the ability to
consistently surveil and control the grid infrastructure. In this paper, we
propose a technique that harnesses power line modems (PLMs) as sensors for
monitoring cable health. We envisage that all or most of these PLMs have
already been deployed for data communication purposes and focus on the
distribution grid or neighborhood area networks in the smart grid. For such a
setting, we propose a machine learning (ML) based framework for automatic cable
diagnostics by continuously monitoring the cable status to identify, assess,
and locate possible degradations. As part of our technique, we also synthesize
state-of-the-art reflectometry methods within the PLMs to extract beneficial
features for effective performance of our proposed ML solution. Simulation
results demonstrate the effectiveness of our solution under different aging
conditions and varying load configurations. Finally, we reflect on our proposed
diagnostics method by evaluating its robustness and comparing it with existing
alternatives.
