Steam turbine vibration fault diagnosis based on particle swarm optimization clustering

Fu Rong Liu*, Hong Wei Wang, Xiao Zhi Gao

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    4 Citations (Scopus)

    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 languageEnglish
    Pages (from-to)9-12
    Number of pages4
    JournalZhendong yu Chongji/Journal of Vibration and Shock
    Volume29
    Issue number8
    Publication statusPublished - 1 Aug 2010
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Fault diagnosis
    • Fuzzy c-means clustering (FCM)
    • Particle swarm optimization (PSO)
    • Steam turbine
    • Vibration

    Fingerprint

    Dive into the research topics of 'Steam turbine vibration fault diagnosis based on particle swarm optimization clustering'. Together they form a unique fingerprint.

    Cite this