Fault analysis of the wear fault development in rolling bearings

Signal processing methods are required to extract the features related to the wear process and how to track its evolution. Several signal processing methods are commonly applied in the experimental and real field tests. The generated signals of these tests are quite complex due to the dynamic nature of wear process, i.e., interaction among different wear mechanisms. Therefore, a dynamic model is required to explain the physical phenomena behind the detected signals. However, the current dynamic models in the literature lack to model the dynamic response under wear deterioration process over the whole lifetime, due to the complexity. Therefore, the purpose of this paper is to illustrate the evolution of the fault features with respect to the wear evolution process. It utilities a newly developed dynamic model and applies different commonly used signal processing methods to extract the diagnostic features of the whole wear evolution progress. The statistical time domain parameters and spectrum analysis are used in this study. Numerical results illustrate several issues related to wear evolution i.e., capabilities, weaknesses and indicators. The results show the extracted fault features and how they change with respect to the wear evolution process i.e., how the topological and tribological changes influence the extracted defect features. In this sense, the study helps to justify the experimental results in literature. The study provides a better understanding of the capability of different signal processing methods and highlights future enhancement.