IntDEM: an intelligent deep optimized energy management system for IoT-enabled smart grid applications
Electrical Engineering,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 24, 2024
Язык: Английский
A robust deep learning system for motor bearing fault detection: leveraging multiple learning strategies and a novel double loss function
Signal Image and Video Processing,
Год журнала:
2025,
Номер
19(4)
Опубликована: Фев. 20, 2025
Язык: Английский
A Deep Learning-Based Cyberattack Detection Method for Line Differential Relays
Internet of Things,
Год журнала:
2025,
Номер
unknown, С. 101574 - 101574
Опубликована: Март 1, 2025
Язык: Английский
Advancements in Grid Resilience: Recent Innovations in AI-Driven Solutions
Results in Engineering,
Год журнала:
2025,
Номер
unknown, С. 105042 - 105042
Опубликована: Апрель 1, 2025
Язык: Английский
Explainable hybrid forecasting model for a 4-node smart grid stability
Energy Reports,
Год журнала:
2025,
Номер
13, С. 4948 - 4961
Опубликована: Апрель 24, 2025
Язык: Английский
A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 30, 2024
Abstract
Motor
bearing
fault
detection
(MBFD)
is
vital
for
ensuring
the
reliability
and
efficiency
of
industrial
machinery.
Identifying
faults
early
can
prevent
system
breakdowns,
reduce
maintenance
costs,
minimize
downtime.
This
paper
presents
an
advanced
MBFD
using
deep
learning,
integrating
multiple
training
approaches:
supervised,
semi-supervised,
unsupervised
learning
to
improve
classification
accuracy.
A
novel
double-loss
function
further
enhances
model’s
performance
by
refining
feature
extraction
from
vibration
signals.
Our
approach
rigorously
tested
on
well-known
datasets:
American
Society
Mechanical
Failure
Prevention
Technology
(MFPT),
Case
Western
Reserve
University
Bearing
Data
Center
(CWRU),
Paderborn
University's
Condition
Monitoring
Damage
in
Electromechanical
Drive
Systems
(PU).
Results
indicate
that
proposed
method
outperforms
traditional
machine
models,
achieving
high
accuracy
across
all
datasets.
These
findings
underline
potential
applying
MBFD,
providing
a
robust
solution
predictive
settings
supporting
proactive
management
machinery
health.
Язык: Английский
Distributed agents structure for current-only adaptive relaying scheme reinforced against failures and cyberattacks
Ain Shams Engineering Journal,
Год журнала:
2024,
Номер
unknown, С. 103143 - 103143
Опубликована: Ноя. 1, 2024
Язык: Английский