Deep learning-based fault classification and location for underground power cable of nuclear facilities

A Said, S Hashima, MM Fouda, MH Saad - Ieee Access, 2022 - ieeexplore.ieee.org
Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise
fault detection systems, especially underground power cable faults, to avoid causing …

Artificial Intelligence Assisted Smart Self‐Powered Cable Monitoring System Driven by Time‐Varying Electric Field Using Triboelectricity Based Cable Deforming …

J Yun, H Cho, I Kim, D Kim - Advanced Energy Materials, 2024 - Wiley Online Library
Cable monitoring is essential for the prevention of machine malfunctions as machines are
operated dynamically. Traditional methods of cable monitoring, conducted through portable …

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things …

D Łuczak, S Brock, K Siembab - Sensors, 2023 - mdpi.com
The human-centric and resilient European industry called Industry 5.0 requires a long
lifetime of machines to reduce electronic waste. The appropriate way to handle this problem …

An efficient cross-terms suppression method in time–frequency domain reflectometry for cable defect localization

XY Zou, HB Mu, HT Zhang, LQ Qu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Time–frequency domain reflectometry (TFDR) is a highly sensitive method to locate the
defects of cables. However, there are cross terms when a multicomponent signal is …

An efficient accuracy improvement method for cable defect location based on instantaneous filtering in time-frequency domain

X Zou, H Mu, R Wang, K Fan, Z Cheng, Y He, G Zhang - Measurement, 2024 - Elsevier
Time-frequency domain reflectometry is a highly sensitive method to locate cable defect.
However, locating a potential defect far from the cable head may be less accurate because …

Detection and characterization of multiple discontinuities in cables with time-domain reflectometry and convolutional neural networks

M Scarpetta, M Spadavecchia, F Adamo, MA Ragolia… - Sensors, 2021 - mdpi.com
In this paper, a convolutional neural network for the detection and characterization of
impedance discontinuity points in cables is presented. The neural network analyzes time …

Partial discharge detection based on optimization of optical probe and Sagnac interference

Y Song, J Jiang, Y He, H Zhou, G Ma… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Partial discharge (PD) measurement is necessary for insulation diagnosis and usually a
required item of the maintenance procedure for most high-voltage equipment assets. Optical …

Machine learning-based fault diagnosis for research nuclear reactor medium voltage power cables in fraction Fourier domain

MH Saad, A Said - Electrical Engineering, 2023 - Springer
Abstract Fault diagnosis of Medium Voltage power Cables (MVCs) research nuclear reactor,
incredibly inaccessible/remote ones, has to be carefully identified, located, and fixed within a …

Optimal Design and Development of Magnetic Field Detection Sensor for AC Power Cable

Y Liu, Y **n, Y Huang, B Du, X Huang, J Su - Sensors, 2024 - mdpi.com
The state detection of power cables is very important to ensure the reliability of the power
supply. Traditional sensors are mostly based on electric field detection. The operation is …

Validation of machine learning-aided and power line communication-based cable monitoring using measurement data

Y Huo, K Wang, L Lampe, VCM Leung - Sensors, 2024 - mdpi.com
The implementation of power line communications (PLC) in smart electricity grids provides
us with exciting opportunities for real-time cable monitoring. In particular, effective fault …