Fault Diagnosis Analysis of Rotor System Based on RBF Neural Network and Dynamic Systems

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

doi:10.4028/www.scientific.net/AMR.460.127

Paper Titles

Steering Control of an Autonomous Vehicle Based on RBF Neural Networks Compensation and Dynamic Systems
p.98

Microstructure and Compound Developed from Ce-As-Fe System at High Temperature
p.103

Prediction of the Effective Elastic Moduli of the Nanoparticle-Reinforced Polymer Matrix Composites Considering Interface Effects
p.107

Nonlinear Dynamic Buckling of FGM Shallow Conical Shells under Triangular Pulse Impact Loads
p.119

Fault Diagnosis Analysis of Rotor System Based on RBF Neural Network and Dynamic Systems
p.127

Preparation and Photocatalytic Preoperty of BaV₂O₆ Photocatalyst by Liquid-Phase Deposition Method
p.131

Wenzhou D-Prefaced Trains Accident and China Work Safety Early Warning Information System Based on Dynamics
p.135

Tensile Damage in Carbon Fabric/Epoxy-Matrix Composites Monitored by Electrical Resistance Measurement Combined with AE Technology
p.140

Influence of Substrate Etching on Structural and Optical Properties of ZnO Nanorods Grown by Hydrothermal Method
p.147

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 460Fault Diagnosis Analysis of Rotor System Based on...

Fault Diagnosis Analysis of Rotor System Based on RBF Neural Network and Dynamic Systems

Abstract:

The RBF network was applied in the rotor system to realize the fault diagnosis aiming the mapping complexity between fault symptoms and fault patterns. It can overcome the problems of low learning rates of convergence and falling easily into part minimums in BP algorithm, and improve the precision of diagnosis. The normalized values of seven frequency ranges in amplitude spectrum were used as the fault characteristic quantity, the RBF network was trained to diagnose the faults of rotor system. The results show that RBF neural network is a valid method of diagnosis of mechanical failure.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 460)

Pages:

127-130

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.460.127

Citation:

Cite this paper

Online since:

February 2012

Authors:

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

Keywords:

Dynamic Systems, Fault Diagnosis, Mechanical Failure, Pattern Recognition, RBF Neural Network, Rotor System

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

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

Google Scholar

[2] Venayagamoorthy G K, Harley R G: 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

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

Google Scholar

[4] Zhang Y, Zhang M, Feng Z, et al: Application and realization of hydraulic pump troubleshooting by using radial basis function neural network, Hydraulic and Pneumatic, (2005), pp.38-40.

Google Scholar

[5] Lu H, Guo X, Du D, et al: Application of radial basis function neural network to fault diagnosis of electronic ejection engine, Transactions of Chinese Society for Agricultural Machinery, Vol. 36(2005), pp.35-38.

Google Scholar

[6] Wang Bao, Ou Fang, Ren De, et al: Research on fault detection of automobile rear drive assembly based on wavelet packet transform decomposition and radial basis function neural network, Mechanism, Vol. 37(2010), pp.29-32.

Google Scholar

[7] Wu H, Zhou N, Wang C: Research for transformer fault diagnosis and MATLAB simulation based on RBF neural networks, Science Technology and Engineering, Vol. 10(2010), pp.1249-1252.

Google Scholar