Publication

Abstract : Fault diagnosis of the roller bearings plays an important role in machine condition monitoring. Researchers are trying to perform the fault diagnosis using machine learning approach. However, there are alternate methods of doing the same. This paper presents the methodology for designing a new wavelet using continuous wavelet transforms. The time domain signals of good bearing is alone required for this study. For the sake of study purpose, three different faults were simulated on to the bearings and the corresponding time domain signals were acquired. The newly designed wavelet gives a classification accuracy of 83.25% in classifying bearing conditions.