A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions

A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions

Rotating machinery is essential in modern industry. A robust noise-resistant method is proposed to improve diagnostic accuracy under intense noise conditions. Initially, time-domain signals are transformed into the time-frequency domain using the Synchrosqueezing Short-Time Fourier Transform to redu...

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Bibliographic Details
Main Authors: Junyu Ren, Soo Siang Teoh
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Artificial intelligence
additive noise
attention mechanisms
convolutional neural networks
data processing
deep learning
Online Access:https://ieeexplore.ieee.org/document/11014518/
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