Research on Fault Diagnosis Based on Neural Network Training by Case Injected GA Algorithm for Rotating Machinery

Xu Sheng Gan; Hua Ping Li

doi:10.4028/www.scientific.net/AMM.602-605.1737

Paper Titles

Nondestructive Detection of Vitamin C Content in Navel Orange Based on GA and GEP Algorithm
p.1718

Research on Grain Storage Insect Image for Automatic Identification
p.1723

Research on Tailings Pond Online Safety Monitoring System Based on Zigbee Sensor Network
p.1728

Research on the Vehicle Detection Technology Based on Improved Burendra Algorithm
p.1733

Research on Fault Diagnosis Based on Neural Network Training by Case Injected GA Algorithm for Rotating Machinery
p.1737

Wavelet Neural Network Based on EKF Algorithm and its Application in Fault Diagnosis for Rotating Machinery
p.1741

Foam Identification Based on Morphological Algorithm and Hough Transform
p.1745

The Research and Application of the Variant Fuzz Testing Framework for Log Based on the Structured Data
p.1749

Realization and Design of Mobile Video Surveillance Client Platform Based on Smart Phone
p.1753

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605Research on Fault Diagnosis Based on Neural...

Research on Fault Diagnosis Based on Neural Network Training by Case Injected GA Algorithm for Rotating Machinery

Abstract:

To diagnose the fault that occurs in rotating machinery, a neural network diagnosis method based on an improved GA algorithm is proposed. In this diagnosis method, a case injected idea is introduced to improve the strong global search capability of traditional GA algorithm; and then the improved GA algorithm is used to optimize the parameters of neural network, fulfilling the training of neural network. Simulation result indicates that, the proposed diagnosis method has a good practicability in the field of fault diagnosis for rotating machinery.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

1737-1740

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.1737

Citation:

Cite this paper

Online since:

August 2014

Authors:

Xu Sheng Gan*, Hua Ping Li

Keywords:

Case Base, Fault Diagnosis, Genetic Algorithm (GA), Neural Network (NN), Rotating Machinery

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] G. Y. Wang, B. J. Xu. Application of RBF neural network in fault diagnosis of rotating machinery. Machinery Design & Manufacture, 9, (2008), 57-58.

Google Scholar

[2] H. J. Yu, C. Z. Chen, S. Zhang, et al. Neural network-based intelligent diagnosis. Beijing: Metallurgical Industry Press, (2000).

Google Scholar

[3] D. E. Rumelhart, J. L. Mcclelland. Parallel distributed processing. Cambridge: MITPress, (1986).

Google Scholar

[4] X. J. Wei, M. Li, G. H. Liu, G. C. Tian. Analysis and research of case injected genetic algorithm. Journal of Nanchang Institute of Aeronautical Technology, 20(1), (2006), 8-11.

Google Scholar

[5] S. J. Louis, J. McDonnell, N. Gizzi. Dynamic strike force asset allocation using genetic algorithms and case-based reasoning. Proceedings of the Sixth Conference on System, Gybernetics, and Informatics, Orlando, (2006), 855-861.

Google Scholar