Spectral clustering method based on network segmentation used in fault diagnosis

被引：0

作者：

Key Laboratory for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China ^{[1
]}

不详 ^{[2
]}

机构：

[1] Key Laboratory for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University

[2] School of Public Policy and Administration, Xi'an Jiaotong University

来源：

Jixie Gongcheng Xuebao | 2008年 / 10卷 / 228-233期

关键词：

Fault diagnosis; Graph segmentation; k-means; Spectral clustering;

D O I：

10.3901/JME.2008.10.228

中图分类号：

学科分类号：

摘要：

The network model of fault diagnosis is put forward, so the data clustering is transformed to network segmentation. And the min-max cut criterion is taken as objective function of segmentation. In view of the disadvantage of the higher computation complexity in the traditional min-max cut criterion optimization algorithm, an algorithm using k-means to improve the process of searching optimal segmentation point is introduced. The applications such as benchmark data and four-stage piston compressor diagnosis problem show that the new algorithm has no strict requirements on data distribution, and can achieve feature extraction and diagnosis fast and effectively.

引用

页码：228 / 233

页数：5

共 13 条

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