Improved K-Means Clustering Method Based on Complex Network for Rolling Bearing Fault Diagnosis

Yang Pan; An Hua Chen; Ling Li Jiang

doi:10.4028/www.scientific.net/AMM.273.250

Paper Titles

Development of Engine Vibration Test Virtual Instrument
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Semi-Discrete Analytical Solution of Lumped Mass Finite Element Method for the Longitudinal Vibrations of an Elastic Bar
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Discuss BMW Automatic Transmission Work Principle and Fault Handling
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Realizing Method of MATLAB Software for Fault Diagnosis of Freight Bearings
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Improved K-Means Clustering Method Based on Complex Network for Rolling Bearing Fault Diagnosis
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Analysis and Manufacture of Skew Bevel Gears with Spherical Involute
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Fault Diagnosis of Rolling Bearing Based on Feature-Level Fusion Method
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An Improved HHT Method and its Application in Fault Diagnosis of Roller Bearing
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The Application of Hybridized DC-DC Converters in the Power Supply System of Spacecraft
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 273Improved K-Means Clustering Method Based on...

Improved K-Means Clustering Method Based on Complex Network for Rolling Bearing Fault Diagnosis

Abstract:

According to the selection difficulties of initial clustering center of k-means clustering algorithm, this paper proposes a method that is to use complex network degree to improve k-means clustering algorithm for fault pattern recognition method, and to improve the accuracy of clustering. Use network to represent fault data structure, with joint connecting matrix to express similarity between nodes, according to the complex concepts of networks degree, calculate the size of every node degree, and select the maximum degree of node as k-means clustering initial center. This method is applied to the rolling bearing clustering diagnosis example, achieving good fault diagnosis effect. This study provides a new method for the selection of initial cluster centers of K-means clustering.