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Prognostics for Ball Bearing Based on Neural Networks and Morlet Wavelet

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Abstract:

This paper deals with a new scheme for prognostics of ball bearing based on Self-Organizing Map (SOM), back propagation neural-network and complex Morlet Wavelet methods. It uses complex Morlet wavelet-based envelope to extract successfully the characteristic frequencies of ball bearing. Then the minimum quantization error (MQE) indicator deriving from SOM is used for performance degradation assessment. Based on Weight Application to Failure Times (WAFT) technology, which deriving from back propagation neural networks, a prognostics model of ball bearing is developed successfully. And the experimental results show that the proposed methods are greatly superior to the currently used L10 bearing life prediction.

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Info:

Periodical:

Materials Science Forum (Volumes 505-507)

Pages:

1153-1158

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.505-507.1153

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Online since:

January 2006

Authors:

Run Qing Huang, Li Feng Xi, C.Richard Liu, Jay Lee

Keywords:

Back-Propagation, Ball Bearing, Morlet Wavelet, Prognostics, Residual Life Prediction, Self-Organizing Maps

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© 2006 Trans Tech Publications Ltd. All Rights Reserved

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