Abstract
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.
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Recommended by Associate Editor Byeng Dong Youn
Nitin Upadhyay received Master of Technology (M.Tech.) in Industrial Engineering and Management from National Institute of Technology, Tiruchirappalli, India. Currently he is a Ph.D. student at the Mechanical Engineering Discipline, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India. His current research areas are Non-linear dynamics, Fault diagnosis of rotor bearing system, condition monitoring and signal processing.
P. K. Kankar is working as an Assistant Professor in Mechanical Engineering Discipline, PDPM-Indian Institute of Information Technology, Design and Manufacturing Jabalpur. He is having more than 10 years of teaching and research experience. He obtained his Ph.D. from the Mechanical and Industrial Engineering Department at Indian Institute of Technology Roorkee, India. His research interests include vibration, design, condition monitoring of mechanical components, nonlinear dynamics, soft computing etc.
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Upadhyay, N., Kankar, P.K. Diagnosis of bearing defects using tunable Q-wavelet transform. J Mech Sci Technol 32, 549–558 (2018). https://doi.org/10.1007/s12206-018-0102-8
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DOI: https://doi.org/10.1007/s12206-018-0102-8