Quadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults

Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing their signal morphology offers insights into their health status. Unfortunately, existing approaches are optimized for controlled environments, neglecting realistic conditions such as time-varying rotational speeds and the vibration's non-stationary nature. This paper presents a fusion of time-frequency analysis and deep learning techniques to diagnose bearing faults under time-varying speeds and varying noise levels. First, we formulate the …

analysis contributors costs cs.ai cs.lg cs.sy diagnosis eess.sy environments health insights machine maintenance signal