一种改进的支持向量数据描述故障诊断方法  

Modified support vector data description for fault diagnosis

作　　者：唐明珠[1] 王岳斌[2] 阳春华[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]湖南理工学院计算机学院,湖南岳阳414006

出　　处：《控制与决策》2011年第7期967-972,共6页Control and Decision

基　　金：国家杰出青年科学基金项目(61025015);国家自然科学基金项目(60874069);国家863计划项目(2009AA04Z137);中南大学优秀博士学位论文扶植项目.

摘　　要：针对故障诊断中故障类样本难于获取以及不均衡类问题,提出了基于粒子群和滑动窗口的支持向量数据描述(M-SVDD)故障诊断方法.该方法利用粒子群优化支持向量数据描述的核参数,同时引入滑动窗口技术,通过大窗口大小来控制故障诊断模型的训练样本数,根据小窗口的预测误差变化动态调整大窗口的大小.采用该方法对铜转炉吹炼过程进行故障诊断的实验结果表明,该方法能有效抑制过拟合现象,具有故障敏感性高、泛化能力强等特点.Aiming at the difficulty of obtaining fault samples and class imbalanced problem,a modified support vector data description for fault diagnosis based on both particle swarm optimization and sliding windows（M-SVDD）is proposed in this paper.The kernel parameters of support vector data description are optimized by the particle swarm optimization. At the same time,the sliding window technique is introduced.The number of training samples for fault diagnosis model is controlled by a dynamic adjusted large window.The size of the large window is adjusted dynamically according to the changes of predicting error of the small window.M-SVDD is applied to the fault diagnosis of copper-converting smelting process.The experimental results show that M-SVDD can prevent effectively the phenomenon of over-fitting and has good fault sensitivity and generalization.

关 键 词：支持向量数据描述 粒子群 故障诊断 不均衡类 滑动窗口 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]