Anomaly Detection for Shielded Cable Including Cable Joint Using a Deep Learning Approach
Seung Jin Chang, Gu-Young Kwon
Abstract
A defect occurring in cable joints is much more severe than a defect on cables due to its high incidence and an intensive electrical stress. In conventional reflectometry, it is hard to distinguish between a reflected signal from normal cable joints and that from faulty cable joints. This paper proposes a novel time–frequency domain reflectometry (TFDR) method based on an unsupervised neural network model combining long short-term memory (LSTM) and variational autoencoder (VAE) that can detect joint defects as well as cable defects. To verify the proposed method, a test bed is constructed with two failure scenarios; (1) defects on cables, and (2) defects in cable joints. In both scenarios, the proposed method successfully detects the failure using an anomaly score that conventional TFDR does not have. The proposed anomaly detection technique is expected to become the cornerstone of systems that can detect anomalies of earlier-stage of defects.