Application of Wavelet Neural Network With Particle Swarm Optimization Algorithm in Boiler Faults Diagnosis

DOI

10.2991/mmebc-16.2016.379How to use a DOI?

Keywords

Particle Swarm Algorithm; Wavelet Neural Network; Faults Diagnosis

Abstract

In view of the main fault type in boiler steam water system, a variety of complex fault data are extracted. A wavelet neural network fault diagnosis based on particle swarm optimization algorithm is designed. The wavelet neural network constructed by three-layer wavelet neural network, is trained by particle swarm algorithm. By optimizing the weights factor, scale factor and shift factor wavelet neural network on particle swarm algorithm, the training speed of wavelet neural network is accelerated and the training accuracy is also improved. The simulation results show that the improved wavelet neural network algorithm is applied to the fault diagnosis of boiler, which can effectively eliminate the influence of redundant connection structure on network diagnostic ability and provide a new way for the boiler fault diagnosis.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yun Du
AU  - Yadong Liu
PY  - 2016/06
DA  - 2016/06
TI  - Application of Wavelet Neural Network With Particle Swarm Optimization Algorithm in Boiler Faults Diagnosis
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 1881
EP  - 1886
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.379
DO  - 10.2991/mmebc-16.2016.379
ID  - Du2016/06
ER  -