Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning

Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning

The vibration signals of faulty bearings contain rich feature information in both the time and frequency domains. Effectively leveraging this information is crucial, especially when addressing imbalanced bearing fault datasets, as it can significantly enhance the performance of fault diagnosis model...

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Bibliographic Details
Main Authors: Huachao Jiao, Wenlei Sun, Hongwei Wang, Xiaojing Wan
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Sensors
Subjects:
adaptive wavelet-like transform
GAN
bearing fault diagnosis
imbalance data
ensemble learning
Online Access:https://www.mdpi.com/1424-8220/25/7/2328
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