Abstract
A novel fuzzy clustering algorithm: PFCM was proposed based on the fusion of particle swarm optimization (PSO) and fuzzy c-means clustering (FCM). The conventional FCM has the two drawbacks of sensitivity to initialization and easily being trapped into local optima, due to the gradient descent approach used. With the features of global optimization and fast convergence, the hybrid algorithm presented can overcome these shortcomings and yield the optimal clustering performance. The new data clustering technique provided was also applied in the vibration fault diagnosis of steam turbine. Computer simulations demonstrate that compared with FCM, the proposed PFCM has a superior fault diagnosis capability.
Original language | English |
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Pages (from-to) | 9-12 |
Number of pages | 4 |
Journal | Zhendong yu Chongji/Journal of Vibration and Shock |
Volume | 29 |
Issue number | 8 |
Publication status | Published - 1 Aug 2010 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Fault diagnosis
- Fuzzy c-means clustering (FCM)
- Particle swarm optimization (PSO)
- Steam turbine
- Vibration