Application of Bearing Diagnosis Based on Wavelet Preprocessing Neural Network

DOI

10.2991/icaset-16.2016.30How to use a DOI?

Keywords

Wavelet preprocessing, Neural network, Parameter modification, Fault diagnosis, Convergence

Abstract

According to compactly supported characteristics of bearing local damage diagnosis based on wavelet analysis, the construction of compactly supported orthogonal function method are studied. With the wavelet multi-resolution analysis and multivariate time series analysis as the foundation, the parameters of weights, scaling and translation in the model are modified. Based on MATLAB, the non-stationary signal of fault bearing acceleration wave is numerical simulated and calculated. Through the comparison of convergence between the parameters revising before and after, it shows that the error on the amplitude and the convergence efficiency of parameter modification are better than the fixed before, which can significantly improve the accuracy of bearing fault diagnosis and signal processing capacity.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiang-bo Chen
AU  - Shi-xian Zeng
AU  - Yan Li
PY  - 2016/05
DA  - 2016/05
TI  - Application of Bearing Diagnosis Based on Wavelet Preprocessing Neural Network
BT  - Proceedings of the 2016 6th International Conference on Applied Science, Engineering and Technology
PB  - Atlantis Press
SP  - 149
EP  - 153
SN  - 2352-5401
UR  - https://doi.org/10.2991/icaset-16.2016.30
DO  - 10.2991/icaset-16.2016.30
ID  - Chen2016/05
ER  -