Advance Fault Identification of Rolling Element Bearing

Abstract

Health monitoring and fault identification of unhealthy bearing have been of great significance in industries. Various techniques to identify faults have been developed, but techniques to filter the vibrations generated due to synchronous noise and random noise precisely are still scarce. This work proposes development of a more precise technique where Morlet Wavelet (MW) is used as a filter for pre-processing of data. Then fast Kurtogram and Hilbert Transform are applied to identify faults on rolling elements. The proposed signal processing algorithm is then implemented using National Instruments LabVIEW for real-time automatic bearing fault detection. The result shows that the fault diagnostics of the proposed technique is more precise.