PSO-SVM及其在减速机齿轮诊断中的应用

PSO-SVM and its application in gear diagnosis

针对支持向量机(SVM)参数一般是人为选取,无法准确取到最佳值的问题,提出了一种基于粒子群算法(PSO)对参数进行优化的支持向量机(PSO-SVM)。以减速机齿轮的3类故障类型(正常、磕碰、磨损)数据作为研究资料,组成训练样本集,训练PSO-SVM分类模型,从训练集中抽取部分数据组成测试样本集,对模型进行检验测试。研究表明:PSO-SVM模型分类正确率达到了93.8%,相较未进行参数优化的SVM,算法能更好地找到全局最优解,提高了模型的分类正确率。

Aiming at the problem that support vector machine（SVM） parameters are given arbitrarily or by means of test experience,and the difficulty in selecting,a SVM which optimize parameter by particle swarm optimization（PSO） algorithm is proposed.Data are equally obtained from three types of faults which are normal,bump and wear are used as research training sample set.PSO-SVM classification sample is trained.Some data records from the train data are used to test the model.The research shows that classification accuracy of PSO-SVM model is 93.8 %.Compared with SVM whose parameters are not optimized,the proposed algorithm can find the optimal solution better and improve the classification accuracy of model.