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Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis

This Reprint presents the latest research progress of signal processing and artificial intelligence technologies for fault diagnosis of high-end mechanical equipment. It contains 13 original research papers, covering advanced dynamics modeling and mechanism analysis methods, novel signal processing...

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Salvato in:
Natura: Online
Lingua:inglese
Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute 2026
Soggetti:
data fusion
axle box bearing
fault diagnosis
characteristic frequency
bandpass filtering
industrial collaborative robot
anomaly detection
trajectory change
autoencoder
dynamic time warping
online monitoring
dual-rotor system
rotor unbalance
assembly phase angle
vibration amplitude
aero-engines
dynamic modeling
aero-engine
neural network
vibration response
fault detection
LSTM
auto-encoder
self-attention mechanism
rolling mill
abnormal vibration
monitoring and diagnosis
multi-sensor information fusion
multi-scale graph convolutional networks
infrared thermography
wind turbine blade
deep learning
defect detection
object localization
YOLO
resonant frequencies
autogram
optimal demodulation frequency band
bearing fault diagnosis
rolling bearing
convolutional neural network
variational Bayesian inference
decision-level fusion
multi-task learning
graph convolutional networks
attention mechanism
hydrodynamic bearing
nonlinear dynamic coefficients
oil film instability
condition monitoring
life prediction
transformer
dual-layer self-attention
multi-scale convolution
traction converter
temperature sensor
coupling fault diagnosis
Accesso online:https://directory.doabooks.org/handle/20.500.12854/170645
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https://directory.doabooks.org/handle/20.500.12854/170645

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