Rolling Bearing

Rolling bearing fault diagnosis is a critical area of research focused on accurately and efficiently identifying bearing defects to prevent catastrophic equipment failures and optimize maintenance schedules. Current research emphasizes the development and application of advanced machine learning models, including convolutional neural networks (CNNs), autoregressive networks, vision transformers, and graph neural networks, often enhanced by techniques like generative adversarial networks (GANs) for data augmentation and knowledge distillation for model compression. These advancements aim to improve diagnostic accuracy, reduce computational complexity, and enable real-time monitoring, ultimately leading to increased reliability and cost savings across various industries.

Papers

March 7, 2022

Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry
Dovile Juodelyte, Veronika Cheplygina, Therese Graversen, Philippe Bonnet
Rolling Bearing Vibration Data Motor Health Monitoring Protein Degradation Full Degradation

December 16, 2021

Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model
V. Sinitsin, O. Ibryaeva, V. Sakovskaya, V. Eremeeva
Deep Learning Efficient Hybrid Accelerometer Signal Rolling Bearing Bearing Fault

Rolling Bearing

Papers

Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry

Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model