The Neural Network Prediction to the Wear and Tear Fault of Thrust Bearing in Air Compressor

Song He Zhang; Yuegang Luo; Bin Wu; Bing Cheng Wang

doi:10.4028/www.scientific.net/AMM.333-335.1758

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 333-335The Neural Network Prediction to the Wear and Tear...

The Neural Network Prediction to the Wear and Tear Fault of Thrust Bearing in Air Compressor

Abstract:

The wear and tear allowances (displacements) of axial thrust bearing in air compressor was diagnosed and predicted, applying the model of artificial neural network (ANN), and compared with the traditional method of diagnosis and prediction. It showed that the results of diagnosis and prediction are more precise than that of traditional method. It can diagnosis and predicts the wear and tear allowances of axial thrust bearing better.

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Periodical:

Applied Mechanics and Materials (Volumes 333-335)

Pages:

1758-1761

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.333-335.1758

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Online since:

July 2013

Authors:

Song He Zhang, Yuegang Luo, Bin Wu, Bing Cheng Wang

Keywords:

Artificial Neural Network (ANN), Diagnosis, Prediction, Tear Allowance, Thrust Bearing, Wear

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References

[1] H. Yu, G. Hou: Expert system for fault diagnosis(Metallurgical industry Press, 1991).

Google Scholar

[2] J. Deng: Grey control system(Huazhong University of Science and Technology Press, 1985).

Google Scholar

[3] S.M. Pandit, .S.M. Wu: Time series and system analysis with application(New York: John wiley and sons, 1983).

Google Scholar

[4] L. Jiao: Apply and realization of neural network(Xi'an University of Electronic Science and Technology Press, 1993).

Google Scholar

[5] J. F. Pollard: Process identification using neural networks, Computer Chemical Engineering, Vol. 16(1992), pp.253-271.

Google Scholar

[6] J. C. Chen: Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms, JASIS, Vol. 46(1995), pp.194-216.

DOI: 10.1002/(sici)1097-4571(199504)46:3<194::aid-asi4>3.0.co;2-s

Google Scholar

[7] S. Ergezinger: An accelerated learning algorithm for multilayer perceptions: layer by layer, IEEE Trans. On Neural Networks, Vol. 6(1995), pp.31-42.

DOI: 10.1109/72.363452

Google Scholar

[8] G. K. Venayagamoorthy, R. G. Harley: A continually online trained neurocontroller for excitation and turbine control of a turbo generator, IEEE Transactions on Energy Conversion, Vol. 16 (2001), pp.261-269.

DOI: 10.1109/60.937206

Google Scholar

[9] J. Wu, S. Liu, K. Deng: A fault diagnosis method based on an improved BP neural network, Industrial Instruments and Automatic Equipment, (2007), pp.45-48.

Google Scholar