By Topic

Research on Data Fusion Diagnosis System Based on Neural Network and D-S Evidence Theory

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xie Chunli ; Forestry Eng. Postdoctoral Flow Station, Northeast Forestry Univ., Harbin ; Guan Qiang

The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosis into three levels, which are data fusion level, feature level and decision level. The feature level uses three parallel neural networks whose structures are the same. The purpose of using neural networks is mainly to get basic probability assignment (BPA) of D-S evidence theory, and the neural networks in feature level are used for local diagnosis. D-S evidence theory integrates the local diagnosis results in decision level. The system diagnosed several main faults of gas turbine rotor on the tester. The results indicate that the diagnosis system can diagnose the faults exactly in real time, and the precision is very high.

Published in:

Testing and Diagnosis, 2009. ICTD 2009. IEEE Circuits and Systems International Conference on

Date of Conference:

28-29 April 2009