Skip navigation
Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/18039
Title: Fault diagnosis of ball bearings using continuous wavelet transform
Authors: Kankar P.K.
Sharma, Satish Chandra
Harsha, Suraj Prakash
Published in: Applied Soft Computing Journal
Abstract: Bearing failure is one of the foremost causes of breakdown in rotating machines, resulting in costly systems downtime. This paper presents a methodology for rolling element bearings fault diagnosis using continuous wavelet transform (CWT). The fault diagnosis method consists of three steps, firstly the six different base wavelets are considered in which three are from real valued and other three from complex valued. Out of these six wavelets, the base wavelet is selected based on wavelet selection criterion to extract statistical features from wavelet coefficients of raw vibration signals. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. Finally, the bearing faults are classified using these statistical features as input to machine learning techniques. Three machine learning techniques are used for faults classifications, out of which two are supervised machine learning techniques, i.e. support vector machine (SVM), artificial neural network (ANN) and other one is an unsupervised machine learning technique, i.e. self-organizing maps (SOM). The methodology presented in the paper is applied to the rolling element bearings fault diagnosis. The Meyer wavelet is selected based on Maximum Energy to Shannon Entropy ratio and the Complex Morlet wavelet is selected using Maximum Relative Wavelet Energy criterion. The test result showed that the SVM identified the fault categories of rolling element bearing more accurately for both Meyer wavelet and Complex Morlet wavelet and has a better diagnosis performance as compared to the ANN and SOM. Features selected using Meyer wavelet gives higher faults classification efficiency with SVM classifier. © 2010 Elsevier B.V. All rights reserved.
Citation: Applied Soft Computing Journal, (2011), 2300- 2312
URI: https://doi.org/10.1016/j.asoc.2010.08.011
http://repository.iitr.ac.in/handle/123456789/18039
Issue Date: 2011
Keywords: Artificial neural network
Energy to Shannon Entropy ratio
Relative Wavelet Energy
Self-organizing maps
Support vector machine
ISSN: 15684946
Author Scopus IDs: 8367238500
8142901100
6603548398
Author Affiliations: Kankar, P.K., Vibration and Noise Control Laboratory, Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247667, Uttaranchal, India
Sharma, S.C., Vibration and Noise Control Laboratory, Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247667, Uttaranchal, India
Harsha, S.P., Vibration and Noise Control Laboratory, Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247667, Uttaranchal, India
Corresponding Author: Harsha, S. P.; Vibration and Noise Control Laboratory, Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247667, Uttaranchal, India; email: surajfme@iitr.ernet.in
Appears in Collections:Conference Publications [ME]

Files in This Item:
There are no files associated with this item.
Show full item record


Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.