针对传统支持向量机(SVM)算法在数据不均衡情况下无法有效实现故障检测的不足，提出一种基于过抽样和代价敏感支持向量机相结合的故障检测新算法。该算法首先利用边界人工少数类过抽样技术(BSMOTE)实现训练样本的均衡。为减少人工增加样本带来的噪声影响，利用K近邻构造一个代价敏感的支持向量机(CSSVM)算法，利用每个样本的代价函数消除噪声样本对SVM算法分类精度的影响。将该算法应用在轴承故障检测中，并同传统的SVM算法，不同类代价敏感SVM-C算法，SVM和SMOTE相结合的算法进行比较，试验结果表明当样本不均衡时，建议算法的故障检测性能较其他算法有显著提高。

Support Vector Machine (SVM) has been extensively studied and have shown remarkable success in fault detection application. However the performance of traditional support vector machine drops significantly when it is applied to the problem of learning from imbalanced datasets where the normal instances heavily outnumbers the fault instances. To address this problem, a novel fault detection approach is proposed which is based on a variant of the Synthetic minority over-sample technique (SMOTE) combined with different error cost-sensitive SVM. As the SVM decision boundary is determined only by a small quantity of support vectors, Consequently, based on SMOTE, this paper presents a new minority over-sample method, in which only the minority examples near the borderline are over-sampled. In order to solve the noise effect, the different error cost-sensitive SVM based on K-Nearest Neighbors (KNN) is adopted to remedy the problem of noise positive instances. The proposed algorithm is applied in bearings fault detection application and is compared against these algorithms along with traditional SVM, different class cost-sensitive SVM (SVM-C), SVM+SMOTE. The experimental results show our approach can achieve better detection performance than other methods.