An Improved PSO Algorithm and its Application on Fault Diagnosis

DOI

10.2991/aiie-15.2015.98How to use a DOI?

Keywords

particle swarm optimization, multi-population, support vector machine, fault diagnosis

Abstract

For the disadvantages of PSO (Particle Swarm Optimization) algorithm, such as premature convergence and easily getting into local extremum, an improved PSO algorithm was presented in this paper. On the one hand, the population with worse performance moved near the global optimization value of the other population with certain probability; on the other hand, one population was randomly chosen to mutate to stimulate the particles jump out the local extremum when the two populations continuously trapped into the same local extremum. The simulation results showed that the improved PSO had a better optimization performance. SVM (Support Vector Machine) trained the improved PSO was applied to fault diagnosis of diesel engine valve. The simulation results showed that the improved PSO-SVM acquired higher accuracy.

Copyright

© 2015, 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  - X.L. Liu
AU  - L.H. Cao
AU  - S.T. Wang
AU  - J.N. Li
AU  - Y. Huang
AU  - Y.P. Li
PY  - 2015/07
DA  - 2015/07
TI  - An Improved PSO Algorithm and its Application on Fault Diagnosis
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 353
EP  - 356
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.98
DO  - 10.2991/aiie-15.2015.98
ID  - Liu2015/07
ER  -