Browsing College of Engineering by Subject "Health Monitoring"
Now showing items 1-1 of 1
-
Integrated Machine Learning Approaches for Comprehensive Bearing Health Monitoring and Fault Classification Using Multi-Sensory Data
(2024 , Master Thesis)Modern industries heavily rely on machines equipped with rolling-element (RE) bearings. However, these machines face substantial risks due to potential bearing faults, where even minor defects can lead to catastrophic ...