A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis

Abstract

The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner.