Improving Bearing Fault Identification by Using Novel Hybrid Involution-Convolution Feature Extraction With Adversarial Noise Injection in Conditional GANs

Improving Bearing Fault Identification by Using Novel Hybrid Involution-Convolution Feature Extraction With Adversarial Noise Injection in Conditional GANs

Bearing faults are critical in machinery; their early detection is vital to prevent costly breakdowns and ensure operational safety. This study presents a pioneering take on addressing the challenges of imbalanced datasets in bearing fault diagnosis. By mitigating issues such as mode collapse and va...

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Bibliographic Details
Main Authors: Muhammad Irfan, Zohaib Mushtaq, Nabeel Ahmed Khan, Faisal Althobiani, Salim Nasar Faraj Mursal, Saifur Rahman, Muawia Abdelkafi Magzoub, Muhammad Armghan Latif, Imran Khan Yousufzai
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Conditional generative adversarial networks
involutional neural networks
convolutional neural networks
mode collapse
vanishing gradients
channel-agnostic
Online Access:https://ieeexplore.ieee.org/document/10288435/

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