基于谱聚类下采样失衡数据下SVM故障检测

Fault detection based on spectral clustering combined with under-sampling SVM under unbalanced datasets

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,试验结果表明所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。

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 was proposed based on spectral clustering combined with SVM under unbalanced samples.In order to classify the unbalanced samples correctly,majority of instances was clustered using spectrum clustering in kernel space for resampling reprentative samples,so as to balance the training samples and enhance the classification performance.The proposed algorithm was applied in fault detection of bearings and the results were compared with those by other methods.The experimental results show that our approach achieves better detection performance than other methods.