Bearing Fault Diagnosis under Varying Work Conditions Based on Synchrosqueezing Transform, Random Projection, and Convolutional Neural Networks

Bearing Fault Diagnosis under Varying Work Conditions Based on Synchrosqueezing Transform, Random Projection, and Convolutional Neural Networks

Bearings are critical components in rotating machinery, and their failure can lead to costly repairs and downtime. To prevent such failures, it is important to detect and diagnose bearing faults early. In recent years, deep-learning techniques have shown promise for detecting and diagnosing bearing...

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Bibliographic Details
Main Authors: Boubker Najdi, Mohammed Benbrahim, Mohammed Nabil Kabbaj
Format: Article
Language:English
Published: The Prognostics and Health Management Society 2024-03-01
Series:International Journal of Prognostics and Health Management
Subjects:
data augmentation
deep learning
signal processing
random projection
synchrosqueezing wavelet transform
convolutional neural networks
predictive maintenance
bearing diagnosis
fault detection
Online Access:https://papers.phmsociety.org/index.php/ijphm/article/view/3799

Internet

https://papers.phmsociety.org/index.php/ijphm/article/view/3799

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