Fault Diagnosis of Roller Bearing Conditions Using ANFIS

Wen Tao Sui; Dan Zhang

doi:10.4028/www.scientific.net/AMM.16-19.886

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 16-19Fault Diagnosis of Roller Bearing Conditions Using...

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.

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Periodical:

Applied Mechanics and Materials (Volumes 16-19)

Pages:

886-890

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.16-19.886

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Online since:

October 2009

Authors:

Wen Tao Sui, Dan Zhang

Keywords:

Adaptive Neuro-Fuzzy Inference System (ANFIS), Fault Diagnosis, Feature Selection, Roller Bearing

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References

[1] W. Wang, F. Ismail and F. Golnaraghi: IEEE Transactions on Fuzzy Systems, Vol. 12 (2004), pp.710-723.

Google Scholar

[2] Information on http: /www. eecs. cwru. edu/laboratory/bearing/ download. htm.

Google Scholar

[3] W.T. Sui, C.H. Lu and D. Zhang: 2008 International Conference on Computer Science and Software Engineering (China, Wuhan, 2008), Vol. 5, pp.518-521.

Google Scholar

[4] J.S.R. Jang: IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23 (1993), pp.665-685.

Google Scholar

[5] X.S. Lou, K.A. Loparo: Mechanical Systems and Signal Processing , Vol. 18 (2004), pp.1077-1095.

Google Scholar

[6] Y.G. Lei, Z.J. He and Y.Y. Zi: Mechanical Systems and Signal Processing, Vol. 21 (2007), pp.2280-2294.

Google Scholar

[7] V.T. Tran and B.S. Yang: Expert Systems with Applications, Vol. 36 (2009) , pp.1840-184.

Google Scholar

[8] J. Liu: An Intelligent System for Bearing Condition Monitoring (Ph.D., University of Waterloo, Canada 2008), pp.1-11.

Google Scholar