Application of a Optimized Wavelet Neural Networks in Rolling Bearing Fault Diagnosis

Yuan Yan Lin; Bin Wu Wang

doi:10.4028/www.scientific.net/AMM.190-191.919

Paper Titles

Accurate Measurement of Photoelasticity Based on Digital Camera
p.902

Adaptive Interacting Multiple Model Unscented Particle Filter Tracking Algorithm
p.906

An Amended Harmony Search Algorithm for Unconstrained Optimization Problems
p.911

Application of a New Soft Measurement in Xylitol Fermentation Process
p.915

Application of a Optimized Wavelet Neural Networks in Rolling Bearing Fault Diagnosis
p.919

Application of Radius Error Compensation Principle in the Profile Measuring Instrument
p.923

Application of Time-Frequency Analysis and Back-Propagation Neural Network in the Lung Sound Signal Recognition
p.927

Application Research on Haigh Diagram in the Wind Turbine Fatigue Analysis
p.931

Based on Estimation of the Ionospheric Characteristics HF Frequency Prediction Method
p.936

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 190-191Application of a Optimized Wavelet Neural Networks...

Application of a Optimized Wavelet Neural Networks in Rolling Bearing Fault Diagnosis

Abstract:

According to the fault type and fault signal of rolling bearing is difficult to predict, the paper proposed a new method to diagnose fault of rolling bearings with the wavelet neural network optimizated by simulated annealing particle swarm optimization. And it was applied to the fault diagnosis of rolling bearing. The experiment shows that this method can reduce the iteration time and improve the accuracy of convergence.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 190-191)

Pages:

919-922

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.190-191.919

Citation:

Cite this paper

Online since:

July 2012

Authors:

Yuan Yan Lin, Bin Wu Wang

Keywords:

Fault Diagnosis, Optimization of Wavelet Network, Rolling Bearing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Hu Kuanggu, Li Shuyu, Su Wanfang. The wavelet neural network and its application [J]. Stereology and Image Analysis, 2006, 6(4): 239-245.

Google Scholar

[2] Mei Hongbin. The rolling bearing vibration monitoring and fault diagnosis [M]. Beijing: Mechanical Industry Press, (2004).

Google Scholar

[3] Mao Zhiwei, Zhang Hua, Jia Jianping. Research on automatic weld seam tracking control simulation for mobile welding robot [J]. Materials Scien and Technology, 2008, 12: 113-116.

DOI: 10.1109/wcica.2008.4593276

Google Scholar

[4] Riko S. Trajectory tracking neural network controller for a robot mecha-nism and Lyapunov theory of stability. Proc of the IEEE Int. Conf on Intelligent Robotics and Systems, Munich, Germany, (2007).

Google Scholar