Machine Learning and Vibration-Based Method for Anti-Friction Bearing Fault Severity Estimation

Abstract

Anti-friction bearings are fundamental components in rotating machinery. Any bearing fault appearing during operation could lead to catastrophic damages and failures without proper maintenance. Numerous methods have been developed for bearing fault detection to reduce maintenance costs and avoid unscheduled downtime. However, once a bearing fault is detected, assessing defect severity may be of more critical concern to industries, as it determines the urgency of interventions such as replacement scheduling and maintenance strategies. This paper presents an efficient estimation method for bearing fault severity using vibration-based input parameters and machine learning. Based on modal characteristics, key input parameters, the vibration amplitudes at the bearing fault frequencies and their harmonics, are extracted from acceleration envelope spectra for their close correlations with physical defect conditions. The nonlinearity between these spectral parameters and bearing fault severity is revealed with experimental observations and is represented using artificial neural networks. The model is validated on experimental vibration data measured from a bearing rig, covering various defect scenarios of different sizes and shapes. The classification criteria of bearing fault severity levels, ranging from healthy to severe, are formulated based on physical defect sizes with maintenance recommendations. Robust and accurate fault severity estimation is achieved across three bearing datasets collected under different operating conditions. The proposed method addresses both fault detection and degradation assessment for anti-friction bearings using simple vibration-based parameters based on rotor and bearing dynamics, providing a practical framework for predictive maintenance in industrial applications.