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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/18041
Title: Rolling element bearing fault diagnosis using complex gaussian wavelet
Authors: Kankar P.K.
Harsha, Suraj Prakash
Sharma, Satish Chandra
Published in: Proceedings of ASME International Mechanical Engineering Congress and Exposition, (IMECE)
Abstract: This paper is focused on fault diagnosis of bearings due to localized defects i.e. spall on the bearing components, which is essential to the design of high performance rotor bearing system. The methodology proposed in this paper for fault diagnosis of rolling element bearings, utilizes autocorrelation of raw vibration signals to reduce the dimension of vibration signals with minimal loss of significant frequency content. Dimension of vibration signal is reduced to 10% with negligible loss of information. To extract most appropriate features from auto-correlated vibration signals and for effective classification of faults, vibration signals are decomposed using complex Gaussian wavelet. Total 150 signals of healthy and defective bearings at rotor speeds 250, 500, 1000, 1500 and 2000 rpm with three loading conditions are considered. 1-D continuous wavelet coefficients of these samples are calculated at the seventh level of decomposition (27 scales for each sample). Maximum Energy to Shannon Entropy ration criterion is used to determine scale corresponding to characteristic defect frequency. Statistical features are extracted from the wavelet coefficients corresponding to selected scales. Finally, bearing faults are classified using Support Vector Machine (SVM) method. The test results show that the SVM can be used efficiently for bearing fault classification. It is also observed that classification accuracy is improved by using autocorrelation. © 2013 ASME.
Citation: Proceedings of ASME International Mechanical Engineering Congress and Exposition, (IMECE), (2010), 1175- 1179. Vancouver, BC
URI: https://doi.org/10.1115/IMECE2010-40476
http://repository.iitr.ac.in/handle/123456789/18041
Issue Date: 2010
Keywords: Classification accuracy
Continuous wavelet
Frequency contents
Localized defects
Rolling Element Bearing
Rotor-bearing system
Statistical features
Wavelet coefficients
Autocorrelation
Defects
Mechanical engineering
Support vector machines
Wavelet transforms
Fault detection
ISBN: 9.78E+12
Author Scopus IDs: 8367238500
6603548398
8142901100
Author Affiliations: Kankar, P.K., Department of Mechanical and Industrial Engineering, Vibration and Noise Control laboratory, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarkhand, India
Harsha, S.P., Department of Mechanical and Industrial Engineering, Vibration and Noise Control laboratory, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarkhand, India
Sharma, S.C., Department of Mechanical and Industrial Engineering, Vibration and Noise Control laboratory, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarkhand, India
Corresponding Author: Kankar, P.K.; Department of Mechanical and Industrial Engineering, Vibration and Noise Control laboratory, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarkhand, India; email: pavankankar@gmail.com
Appears in Collections:Conference Publications [ME]

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