Detection and diagnosis of bearing defects using vibration signal processing

This work presents an analysis of vibration signals for bearing defects using a proposed approach that includes several methods of signal processing. The goal of the approach is to efficiently divide the signal into two distinct components: a meticulously organized segment that contains relatively straightforward information, and an inherently disorganized segment that contains a wealth of intricately complex data. The separation of the two component is achieved by utilizing the weighted entropy index (WEI) and the SVMD algorithm. Information about the defects was extracted from the envelope spectrum of the ordered and disordered parts of the vibration signal. Upon applying the proposed approach to the bearing fault signals available in the Paderborn university database, a high amplitude peak can be observed in the outer ring fault frequency (45.9 Hz). Likewise, for the signals available in XJTU-SY, a peak is observed at the fault frequency (108.6 Hz).