GAN-Based Data Augmentation for Fault Diagnosis and Prognosis of Rolling Bearings: A Literature Review

GAN-Based Data Augmentation for Fault Diagnosis and Prognosis of Rolling Bearings: A Literature Review

<p dir="ltr">In 2014, Goodfellow et al. introduced Generative Adversarial Networks (GANs), an adversarial learning framework designed to generate synthetic data. In rolling bearing fault diagnosis and prognosis, specific GAN variants such as Conditional GANs (cGANs), Wasserstein GANs...

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Bibliographic Details
Main Author: Md. Sulyman Islam Sifat (22928983) (author)
Other Authors: Md Alamgir Kabir (13400748) (author), M. M. Manjurul Islam (22928986) (author), Atiq Ur Rehman (8843024) (author), Amine Bermak (1895947) (author)
Published: 2025
Subjects:
Engineering
Mechanical engineering
Information and computing sciences
Artificial intelligence
Machine learning
Data augmentation
predictive maintenance
rolling bearings
fault diagnosis and prognosis
generative adversarial networks
Training
Generative adversarial networks
Systematic literature review
Generators
Convolutional neural networks
Best practices
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