Paper Title:
Fault Diagnosis of Roller Bearing Conditions Using ANFIS
  Abstract

This paper presents a fault diagnosis method on roller bearings based on adaptive neuro-fuzzy inference system (ANFIS) in combination with feature selection. The class separability index was used as a feature selection criterion to select pertinent features from data set. An adaptive neural-fuzzy inference system was trained and used as a diagnostic classifier. For comparison purposes, the back propagation neural networks (BPN) method was also investigated. The results indicate that the ANFIS model has potential for fault diagnosis of roller bearings.

  Info
Periodical
Edited by
Kai Cheng, Yongxian Liu, Xipeng Xu and Hualong Xie
Pages
886-890
DOI
10.4028/www.scientific.net/AMM.16-19.886
Citation
W. T. Sui, D. Zhang, "Fault Diagnosis of Roller Bearing Conditions Using ANFIS", Applied Mechanics and Materials, Vols. 16-19, pp. 886-890, 2009
Online since
October 2009
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