A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations

• Main Authors: Hakim M., Omran A.A.B., Ahmed A.N., Al-Waily M., Abdellatif A.
• Other Authors: 58938943800
• Format: Review
• Published: Ain Shams University 2024
• Subjects: Deep learning; Fault diagnosis; Rolling bearing; Systematic review; Transfer learning; Convolutional neural networks; Fault detection; Production efficiency; Recurrent neural networks; Roller bearings; Accident rate; Bearing fault; Bearing fault detection; Bearing fault diagnosis; Faults diagnosis; Rolling bearings; Systematic Review; Failure analysis

Rolling bearing fault detection is critical for improving production efficiency and lowering accident rates in complicated mechanical systems, as well as huge monitoring data, posing significant challenges to present fault diagnostic technology. Deep Learning is now an extraordinarily popular research topic in the field and a promising approach for detecting intelligent bearing faults. This paper aims to give a comprehensive overview of Deep Learning (DL) based on bearing fault diagnosis. The most widely used DL algorithms for detecting bearing faults include Convolutional Neural Network, Recurrent neural network, Autoencoder, and Generative Adversarial Network. It discusses a variety of transfer learning architectures and relevant theories while summarises, classifies, and explains several publications on the subject. The research area's applications and problems are also addressed. � 2022 THE AUTHORS