Diagnosis Technique Based on BP and D-S Theory

Abstract:

Article Preview

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 et al., "Diagnosis Technique Based on BP and D-S Theory", Advanced Materials Research, Vols. 179-180, pp. 544-548, 2011

Online since:

January 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.