Condenser Fault Diagnosis Based on FNN and Data Fusion

Abstract:

Article Preview

In order to improve the fault diagnosis result of the condenser, one new approach based on the fuzzy neural network and data fusion is proposed in this paper. Firstly, the data from the various sensors can be processed through the specific membership functions. With the fault symptoms and fault types of condenser, the fuzzy neural network is constructed for the primary fault diagnosis. Some likelihood of the neural network outputs is too close to make the correct decision of fault diagnosis. The problem can be solved by the data fusion technology. This method was successfully adopted in the application of condenser fault diagnosis. Compared with the general method of FNN, this approach can enhance the accuracy in the domain of fault diagnosis, especially for reducing the uncertainty in the fault diagnosis.

Info:

Periodical:

Edited by:

Ran Chen

Pages:

3762-3766

DOI:

10.4028/www.scientific.net/AMM.44-47.3762

Citation:

F. Xia et al., "Condenser Fault Diagnosis Based on FNN and Data Fusion", Applied Mechanics and Materials, Vols. 44-47, pp. 3762-3766, 2011

Online since:

December 2010

Export:

Price:

$35.00

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

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