This systematic review aims to investigate recent developments in the area of arc fault
detection. The rising demand for electricity and concomitant expansion of energy systems …

Machine learning is an expanding field with an ever-increasing role in everyday life, with its
utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this …

Feature selection has been considered as an effective method to solve imbalanced
classification problems. It can be formulated as a multiobjective optimization problem (MOP) …

Digital Transformation integrates information technology across a broad spectrum of
industrial sectors. Industrial Cyber-Physical Systems (ICPS) play a vital role in this …

Compared with traditional model-based fault detection and classification (FDC) methods,
deep neural networks (DNNs) prove to be more accurate for aerospace sensors. An …

In power energy distribution networks, switchgear is considered critical equipment. This is
because the act of monitoring the operation and condition of switchgear, as well as …

Arc faults are serious discharges, damaging insulation systems, and triggering electrical
fires. This is a transversal topic, affecting from residential to aeronautic applications. Current …

This article investigates into signal regularity in ac series arc fault (SAF) detection. The
regularity can solve the vital problem that SAF current characteristics change or disappear in …

Detecting series arcing faults in the electrical networks of aircraft can help mitigate dramatic
consequences such as fires. Non-Artificial Intelligence algorithms often fail to generalise due …

DC series arc fault is one of the main causes of low-voltage dc distribution system fire
accident. Traditional methods use time-frequency domain features to make judgments …