Rolling Element Bearings (REB) are widely used machine components in the vast majority of rotating machines. The condition monitoring of bearings is very impotant for assessing the overall machine performance. Vibration-based signal analysis methods have been widely used for bearing fault diagnosis for several decades. Various vibration signature analysis techniques have been developed for single defect diagnosis of REB. There is a lot of scope of investigation in multiple fault detection for REB. In this present work, an investigation is made on the time and frequency domain techniques for fault identification of REB. Vibration signatures were collected from the housing of bearing in four bearing conditions. The bearings condition considered as a healthy bearing and bearing with outer race, inner race and rolling element defects. The objective of this work is to investigate the feasibility of Fast Fourier Transform (FFT) and band pass analysis for fault identification of REB with multiple faults. Experiments under three faulty and healthy conditions have confirmed that the filtered signals under three frequency bands can be valuable signatures for fault identification and the Root Mean Square (RMS) values of filtered signals can be further utilized as parameters of diagnostic importance.
The
vibration signals obtained from the vicinity of a bearing
assembly contain rich information about the bearing signature.
Vibration monitoring is based on the principle that all systems
produce vibration. When a machine is operating properly, vibration
is small and constant; however, when faults develop and some
of the dynamic process in the machine change, the vibration
spectrum also changes. Effective identification of bearing
condition is, however, not so straightforward. This motivates
to choose bearing fault diagnosis through vibration analysis. |
