Paper Title:
Garbage Crusher Fault Diagnosis Based on RBF Neural Network
  Abstract

The garbage crusher is a new kind of crusher for garbage crushing when processing Municipal Solid Waste (MSW). With the development of automatic equipment and the complication of structure and properties of the garbage crusher, the fault diagnosis of garbage crusher is very important. In this paper, according to the fault symptoms and parameters, Radial Basis Function Neural Network (RBF NN) is used for fault diagnosis of the garbage crusher. The structure and inference of RBF NN are discussed in detail. The garbage crusher fault diagnosis model is established based on RBF network. At last, the fault of mechanical system is taken as an example of garbage crusher fault diagnosis. Training simulation results of the neural network are given base on MATLAB software. The result shows the RBF NN is suitable for fault diagnosis of garbage crusher.

  Info
Periodical
Edited by
Kai Cheng, Yongxian Liu, Xipeng Xu and Hualong Xie
Pages
971-975
DOI
10.4028/www.scientific.net/AMM.16-19.971
Citation
Y. H. Sun, C. Li, M. F. Huang, H. Jing, "Garbage Crusher Fault Diagnosis Based on RBF Neural Network", Applied Mechanics and Materials, Vols. 16-19, pp. 971-975, 2009
Online since
October 2009
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$32.00
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