The Improved K-Means Cluster Analysis on Diagnosis Data Fusion of the Aero-Engine

Xiao Bo Liu; Bei Bei Deng; Liang Ni Shen

doi:10.4028/www.scientific.net/AMM.328.463

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Numerical Simulation on the Shape of Stamping Part about Hot Forming and Quenching
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The Improved K-Means Cluster Analysis on Diagnosis Data Fusion of the Aero-Engine
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 328The Improved K-Means Cluster Analysis on Diagnosis...

The Improved K-Means Cluster Analysis on Diagnosis Data Fusion of the Aero-Engine

Abstract:

Aiming at the problem about initial clustering center was randomly assigned in K-means clustering algorithm, the improved K-means clustering algorithm based on hierarchical clustering algorithm and K-means clustering algorithm was proposed in this paper. In the improved algorithm, first of all K was calculated by hierarchical clustering. When K was determined, K-means clustering was implemented. The results of the aero-engine vibration data clustering shown that not only the k value was to quickly and accurately determined, but also the number of clusters can be reduced and higher computing efficiency can be attained by the improved K-means clustering algorithm.

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Periodical:

Applied Mechanics and Materials (Volume 328)

Pages:

463-467

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.328.463

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Online since:

June 2013

Authors:

Xiao Bo Liu, Bei Bei Deng, Liang Ni Shen

Keywords:

Aero-Engine, Hierarchical Clustering Algorithm, Improved K-Means Clustering Algorithm, Initial Clustering Center

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