【摘要】 故障样本不足是制约故障诊断技术向智能化方向发展的主要原因之一 .支持向量机 (SVM)是一种基于统计学习理论 (SL T)的机器学习算法 ,它能在训练样本很少的情况下达到很好的分类效果 ,从而为故障诊断技术向智能化发展提供了新的途径 .本文介绍了支持向量机分类算法 ,以滚动轴承的故障分类为例 ,探讨了该算法在故障诊断领域中的应用 ,并与 BP神经网络分类方法进行了对比研究 .结果表明 ,SVM方法在少样本情况下的分类效果优于 BP神经网络分类方法 .更多还原

【Abstract】 Shortage of fault samples is one of the main reasons that restrict the development of intelligent fault diagnosis. The support vector machine (SVM) is a machine learning algorithm based on the statistical learning theory (SLT), which has desirable classification ability even if with fewer samples. SVM provides us with a new method to develop the intelligent fault diagnosis. In this paper, the classification algorithm of support vector machines and its application in fault diagnosis are discussed. The result of rolling bearing fault diagnosis by using SVM is compared with that by using BP neural network, which shows that the SVM has higher classification adaptability than BP neural network in the case of fewer samples.更多还原