Abstract
In this paper, fault classification was done using RBIO 5.5 wavelet. Features were extracted at fifth level of decomposition with wavelet packet transform (WPT) where energy and kurtosis were extracted for both horizontal and vertical responses at all WPT nodes. Thus, total 400 samples were taken of defective bearing with reference to healthy bearing to minimize the experimental error. Multilayer perceptron of ANN with correlation-based feature selection has compared with sequential minimal optimization-based support vector method (SVM). Result shows that ANN with multilayer perceptron with CFS criteria has performed better than SVM for classification of ball bearing condition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Samanta, B.K., Al-Balushi, R.: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Signal Process. 17(2), 317–328 (2003)
Lin, J., Qu, Liangsheng: Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. J. Sound Vib. 234(1), 135–148 (2000)
Wu, T.Y., Chen, J.C., Wang, C.C.: Characterization of gear faults in variable rotating speed using Hilbert-Huang transform and instantaneous dimensionless frequency normalization. Mech. Syst. Signal Process. 30, 103–122 (2012)
Nikolaou, N.G., Antoniadis, I.A.: Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets. Mech. Syst. Signal Process. 16(4), 677–694 (2002)
Prabhakar, S., Mohanty, A.R., Sekhar, A.S.: Application of discrete wavelet transform for detection of ball bearing race faults. Tribol. Int. 35(12), 793–800 (2002)
Purushotham, V., Narayanan, S., Suryanarayana, A.N.P.: Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT E Int. 38(8), 654–664 (2005)
Saravanan, N., Kumar Siddabattuni, V.N.S., Ramachandran, K.I.: A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box. Expert Syst. Appl. 35(3), 1351–1366 (2008)
Lei, Y., He, Z., Zi, Y.: Application of an intelligent classification method to mechanical fault diagnosis. Expert Sys. Appl. 36(6), 9941–9948 (2009)
Rafiee, J., et al.: A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system. Expert Syst. Appl. 36(3), 4862–4875 (2009)
Rafiee, J., Rafiee, M.A., Tse, P.W.: Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Syst. Appl. 37(6), 4568–4579 (2010)
Meyer, D., Friedrich, L., Kurt, H.: The support vector machine under test. Neurocomputing. 55(1), 169–186 (2003)
Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis. The University of Waikato (1999)
Pandya, D.H., Upadhyay, S.H., Harsha, S.P.: Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN. Expert Syst. Appl. 40(10), 4137–4145 (2013)
Pandya, D.H., Upadhyay, S.H., Harsha, S.P.: Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft. Comput. 18(2), 255–266 (2014)
Pandya, D.H., Upadhyay, S.H., Harsha, S.P.: Fault diagnosis of high-speed rolling element bearings using wavelet packet transform. Int. J. Signal Imaging Syst. Eng. 8(6), 390–401 (2015)
Acknowledgements
This work is supported by LDRP Institute of Technology & Research a constituent institute of Kadi Sarva Vishwavidyalaya, Gandhinagar, Gujarat.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Darji, A., Darji, P.H., Pandya, D.H. (2019). Fault Diagnosis of Ball Bearing with WPT and Supervised Machine Learning Techniques. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_25
Download citation
DOI: https://doi.org/10.1007/978-981-13-0923-6_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0922-9
Online ISBN: 978-981-13-0923-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)