基于特征抽取和SVM分类器的故障诊断

为了提高支撑向量机(Support Vector Machine,SVM)的性能,降低时间开销;提出一种基于特征提取的SVM算法,并将其用于汽轮发电机组的故障诊断;使用KFDA(Kernel Fisher Discriminant Analyst)算法提取汽轮发电机组数据的关键特征,并使用SVM分类器对特征数据集合进行分类检测;实验结果表明:算法是可行和有效的,在分类性能和训练时间上都得到了提高。

Fault Diagnostics Based on Features Extraction and SVM Classifier

In order to improve the support vector machine (Support Vector Machine, SVM) performance, reduce the time cost, this paper proposes a SVM algorithm based on feature extraction, and uses it for fault diagnosis of steam turbine generator set. Firstly, Kernel Fisher Discriminant Analyst (KFDA) algorithm is used to extract the key features of the turbine generator set data, and then the SVM classifier is used to classify the feature data set. Experimental results show that the algorithm is feasible and effective, and it has been improved in classification performance and detection time.