Paper Title:
Diagnosis Technique Based on BP and D-S Theory
  Abstract

This paper constructs a common data fusion framework of fault diagnosis, by combining local neural networks with dempster-shafer (D-S) evidential theory. The RBF neural network is proposed as a local neural network of the fault pattern recognition, and its input vectors are extracted by the wavelet packet decomposition of various frequency energy. Then, the signal of each sensor separately has a feature level fusion. This method is effective, verified by experiments. The given decision level fusion is based on combining the features of the neural network and the D-S theory, and experiments show the results of the fault diagnosis are more accurate by this method.

  Info
Periodical
Advanced Materials Research (Volumes 179-180)
Edited by
Garry Zhu
Pages
544-548
DOI
10.4028/www.scientific.net/AMR.179-180.544
Citation
Q. Y. Mo, J. Cao, F. Gao, "Diagnosis Technique Based on BP and D-S Theory", Advanced Materials Research, Vols. 179-180, pp. 544-548, 2011
Online since
January 2011
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Price
$32.00
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