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

Autor/innen

  • Kushal Goyal
  • Pratesh Jayaswal Department of Mechanical Engineering, Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India

Schlagwörter:

Fault identification, rolling element bearing, feature extraction, artificial neural networks, wavelet transform

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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Veröffentlicht

2017-06-26

Ausgabe

Bd. 7 Nr. 2 (2017)

Rubrik

Research Articles

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