Paper Title:
Fault Diagnosis Based on Particle Swarm Fuzzy Clustering Algorithm
  Abstract

Fuzzy c-means clustering algorithm (FCM) is sensitive to noise and less effective when handling high dimensional data set. Given that particle swarm optimization algorithm (PSO) has strong global search capability and efficient performance, a new PSO based fuzzy clustering algorithm is proposed. Particles in the new algorithm are encoded by membership in FCM. The new algorithm adopts a new strategy to meet the constraints of FCM, so as to optimize the clustering effect of FCM. Finally, this algorithm is applied to motor fault diagnosis. Experiment shows that the new algorithm made up for the shortcomings of FCM, improved the efficiency and accuracy of clustering and bettered fault diagnosis results.

  Info
Periodical
Edited by
Helen Zhang and David Jin
Pages
111-114
DOI
10.4028/www.scientific.net/AMM.63-64.111
Citation
Z. X. Wang, Q. Niu, "Fault Diagnosis Based on Particle Swarm Fuzzy Clustering Algorithm", Applied Mechanics and Materials, Vols. 63-64, pp. 111-114, 2011
Online since
June 2011
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