Semi-supervised kernel-based fuzzy clustering for gear outlier detection

被引：0

作者：

School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China ^{[1
]}

机构：

[1] School of Mechanical and Automotive Engineering, South China University of Technology

来源：

Jixie Gongcheng Xuebao | 2009年 / 10卷 / 48-52期

关键词：

Fuzzy clustering; Kernel function; Outlier detection; Semi-supervised learning;

D O I：

10.3901/JME.2009.10.048

中图分类号：

学科分类号：

摘要：

Kernel clustering is investigated in mechanical fault detection, and a semi-supervised kernel-based fuzzy clustering method is presented for gear fault early detection. The difficulty in early detection of mechanical incipient fault is to extract the weak fault information in noises. The semi-supervised kernel clustering method utilizes a few of known samples, combined with a larger amount of unknown samples to perform semi-supervised learning, and obtains good efficiency. The experiments are conducted on a gearbox, where a surface defect of tooth pitting is introduced. The result of semi-supervised kernel clustering is compared with that of unsupervised kernel clustering, which demonstrates the superiority of the semi-supervised method for gear failure detection.

引用

页码：48 / 52

页数：4

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