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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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Md. Sulyman Islam Sifat (22928983) (author)
مؤلفون آخرون:	Md Alamgir Kabir (13400748) (author), M. M. Manjurul Islam (22928986) (author), Atiq Ur Rehman (8843024) (author), Amine Bermak (1895947) (author)
منشور في:	2025
الموضوعات:	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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GAN-Based Data Augmentation for Fault Diagnosis and Prognosis of Rolling Bearings: A Literature Review

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