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Fault Diagnosis of Ball Bearing with WPT and Supervised Machine Learning Techniques

  • Ankit DarjiEmail author
  • P. H. Darji
  • D. H. Pandya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

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.

Keywords

Signal processing Fault diagnosis MLT WPT ANN SVM 

Notes

Acknowledgements

This work is supported by LDRP Institute of Technology & Research a constituent institute of Kadi Sarva Vishwavidyalaya, Gandhinagar, Gujarat.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.C. U. Shah UniversityWadhwan, SurendranagarIndia
  2. 2.Mechanical DepartmentC. U. Shah College of Engineering & TechnologyWadhwan, SurendranagarIndia
  3. 3.Mechanical DepartmentLDRP-ITRGandhinagarIndia

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