Bearing Fault Detection with Edge AI

Specialized predictive maintenance focused on detecting early-stage bearing failures in rotating machinery. Analyzes high-frequency vibration signatures from accelerometers to classify fault types including inner race defects, outer race defects, ball defects, and cage failures before catastrophic failure occurs. Models process spectral features extracted from vibration data windows. The CWRU Bearing Dataset is the standard benchmark for training and evaluating bearing fault classifiers.