LAUSR

Defects in rolling element bearings are foremost cause of failure in rotating machines. The accurate and fast diagnosis of bearing defects like spall, dents, pits, cracks etc. on the various component of bearing can be accomplished by analysis of vibration signals using various advanced signal processing techniques. In this work, a new technique for the diagnosis of bearing defects using tunable Q-wavelet transform and fractal based features has been presented. The vibration signals have been recorded experimentally. These signals are decomposed into a number of sub-bands using tunable Q-wavelet transform for effective feature extraction. Classical statistical features and fractal dimension based features such as Higuchi fractal dimensions and Katz fractal dimensions are computed for each decomposed sub-band. These features obtained using tunable Q-wavelet transform of vibration signal are having better capability to classify defects through various machine learning algorithms.