Trends in Mechanical Engineering & Technology, Vol 7, No 2 (2017)

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Fault Identification by Wavelet Feature Extraction in Self Aligning Rolling Element Bearings Using Neural Networks

Kushal Goyal, Pratesh Jayaswal

Abstract


This paper aims to present a comparison of different individual defects in self aligning ball bearings by the use of statistical tools and machine learning techniques like artificial neural network (ANN). The results generated are analyzed, and more realistic conformance to the theoretical observations has been drawn. Vibration analysis of a fault affected component gives a good understanding of machine diagnostics. Inner race and outer race defects have been studied in this paper. This study suggests a method of feature extractions by using wavelet transform and then an algorithm based on ANN which verified the experimentation. ANN architecture also avoided inappropriate classification while calculating the defect value.

 

Cite this Article

 

Kushal Goyal, Pratesh Jayaswal. Fault Identification by Wavelet Feature Extraction in Self Aligning Rolling Element Bearings Using Neural Networks. Trends in Mechanical Engineering & Technology. 2017; 7(2): 11–17p.


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