Rolling element bearing feature extraction and anomaly detection based on vibration monitoring

In this paper, an anomaly detection structure, in which different types of anomaly detection routines can be applied, is proposed. Bearing fault modes and their effects on the bearing vibration are discussed. Based on this, a feature extraction method is developed to overcome the limitation of time domain features. Experimental data from bearings under different operating conditions are used to verify the proposed method. The results show that the extracted feature has a monotonic decrease trend as the dimension of fault increases. The feature also has the ability to compensate the variation of rotating speed. The proposed structure are verified with three different detection routines, pdf-based, k-nearest neighbor, and particle-filter-based approaches.