单类支持向量机在涡轮泵试车数据分析中的应用

Application of One-Class Support Vector Machines to Fault Analysis of Turbopump Test Data

针对在火箭发动机涡轮泵的故障检测过程中缺乏故障样本的问题,应用单类支持向量机,为高速涡轮泵试车数据分析建立了一种新异类检测超球模型。该模型在仅对正常数据进行训练的基础上便可以进行分类工作,且为了提高训练效率,提出了一种分层式的快速训练方法。对火箭发动机涡轮泵试车数据分析的应用结果表明,该算法具有很高的训练效率,检测结果良好。

To deal with the lack of fault samples in the fault detection of a Liquid Rocket Engine(LRE)turbopump,a detection model based on one-class support vector machines(OC-SVMs)was founded.As a novel detector,the model requires no negative training instances and is able to solve classification tasks on the basis of positive samples only.And a multi-layer high speed training strategy was proposed for the improvement of training efficiency.The analysis of LRE historical test data showed that the algorithm has a high training efficiency and can detect the faults of the LRE turbopump.