基于谱聚类下采样失衡数据下SVM故障检测

陶新民;张冬雪;郝思媛;徐鹏

振动与冲击 ›› 2013, Vol. 32 ›› Issue (16) : 30-36.

PDF(1372 KB)
PDF(1372 KB)
振动与冲击 ›› 2013, Vol. 32 ›› Issue (16) : 30-36.
论文

基于谱聚类下采样失衡数据下SVM故障检测

  • 陶新民1,张冬雪1,郝思媛1,徐鹏1
作者信息 +

Fault Detection based on Spectral Clustering under-sampling SVM under unbalanced datasets

  • TAO Xin-min1 ZHANG Dong-xue 1 HAO Siyuan 1 Xu peng1
Author information +
文章历史 +

摘要

在故障诊断领域中,对传统支持向量机(SVM)算法在数据失衡情况下无法有效实现故障检测的不足,提出一种基于谱聚类下采样失衡数据下SVM故障检测算法。该算法在核空间中对多数类进行谱聚类,然后选择具有代表意义的信息点,最终实现样本均衡。将该算法应用在轴承故障检测领域,并同其他算法进行比较,试验结果表明本文建议的算法在失衡数据情况下较其他算法具有较强的故障检测性能。

Abstract

In fault diagnosis application, the performance of traditional support vector machine (SVM)drops significantly when it is applied to the problem of learning from imbalanced datasets where the fault instances heavily outnumbers the normal instances. To address this problem, a novel fault detection SVM approach is proposed which is based on spectral clustering combined with SVM under unbalanced samples. In order to classify the unbalanced samples correctly. Majority instances are clustered using spectrum cluster in kernel space for resampling reprentative samples, so as to balance the training samples and enhance the classification performance. The proposed algorithm is applied in fault detection of bearings and is compared against other methods. The experimental results show that our approach achieves better detection performance than other methods.

关键词

故障检测 / 谱聚类 / 下采样 / 失衡数据

Key words

fault detection / spectral Clustering / under-sample / unbalanced samples

引用本文

导出引用
陶新民;张冬雪;郝思媛;徐鹏. 基于谱聚类下采样失衡数据下SVM故障检测[J]. 振动与冲击, 2013, 32(16): 30-36
TAO Xin-min ZHANG Dong-xue HAO Siyuan Xu peng. Fault Detection based on Spectral Clustering under-sampling SVM under unbalanced datasets[J]. Journal of Vibration and Shock, 2013, 32(16): 30-36

PDF(1372 KB)

Accesses

Citation

Detail

段落导航
相关文章

/