Analysis of Two Neural Networks in the Intelligent Faults Diagnosis of Metallurgic Fan Machinery

Authors:
Jiangang Yi;Peng Zeng
Affiliations:
College of Machinery & Automation, Wuhan University of Science and Technology, Wuhan, China 430081;College of Mathematics & Computer Science, Jianghan University, Wuhan, China 430056
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Abstract

With the aim to study the faults diagnosis ability of the BPNN and the RBFNN, many experiments are done to test the learning ability, the diagnosis ability and the anti-noise ability. The analysis shows the RBFNN has better learning ability and anti-noise ability than the BPNN. However, in the process of concurrent faults diagnosis, both have bad recognition rate. A realistic application verifies the single neural network can not used for metallurgic fan machinery faults diagnosis.