Improving Bearing Fault Identification by Using Novel Hybrid Involution-Convolution Feature Extraction With Adversarial Noise Injection in Conditional GANs-Reference-Cited by-同舟云学术

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

Published:2023 Issue: Volume:11 Page:118253-118267
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Irfan Muhammad¹^ORCID,Mushtaq Zohaib²^ORCID,Khan Nabeel Ahmed³^ORCID,Althobiani Faisal⁴,Mursal Salim Nasar Faraj¹,Rahman Saifur¹^ORCID,Magzoub Muawia Abdelkafi⁵^ORCID,Latif Muhammad Armghan⁶,Yousufzai Imran Khan²^ORCID

Affiliation:

1. Electrical Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia

2. Department of Electrical, Electronics and Computer Systems, College of Engineering and Technology, University of Sargodha, Sargodha, Pakistan

3. Department of Electrical Engineering, Riphah International University, Islamabad, Pakistan

4. Faculty of Maritime Studies, King Abdulaziz University, Jeddah, Saudi Arabia

5. Sudan Technological University, Omdurman, Sudan

6. Department of Computer and Information Systems, Cleveland State University, Cleveland, OH, USA

Funder

Deanship of Scientific Research, Najran University, Saudi Arabia, through the Distinguished Research Funding Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10288435.pdf?arnumber=10288435

Reference57 articles.

1. Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study

2. An Intelligent Fault Diagnosis Method Enhanced by Noise Injection for Machinery

3. An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition

4. A Novel Method for Imbalanced Fault Diagnosis of Rotating Machinery Based on Generative Adversarial Networks

5. Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection

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