基于支持向量机的航空发动机转静碰摩部位诊断规则提取
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基础研究项目(613139);国家自然科学基金(61179057)


Aero-engine rotor-stator rubbing positions diagnosis rule acquisition based on support vector machine
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    摘要:

    引入基于支持向量机(SVM)的数据挖掘技术,提出了基于SVM的转静碰摩部位诊断知识获取.首先,基于带机匣的航空发动机转子实验器,模拟了4个碰摩部位的碰摩实验,利用机匣4个部位的应变测试,获取了4个碰摩部位和4个测点的大量实验数据;然后提出了一种基于支持向量聚类(SVC)的诊断知识规则提取方法.在该方法中,利用SVC算法得到特征选取后样本的聚类分配矩阵,最后根据聚类分配矩阵构建超矩形规则.为使规则更加简洁,易于解释,采用规则合并、维数约简、区间延伸等方法对超矩形规则进行进一步简化.利用基于SVM的数据挖掘方法,从大量的碰摩部位实验数据中提取出了转静碰摩部位诊断的知识规则,并进行了相应解释和验证,规则识别率达到了99%以上,表明了该方法的正确有效性.

    Abstract:

    The data mining technology based on support vector machine(SVM) was introduced,and aero-engine rotor-stator rubbing positions diagnosis rule acquisition was proposed based on SVM.Firstly,the rubbing experiment of 4 rubbing parts was simulated based on aero-engine rotor tester with the casing,and a large number of experimental data were obtained by using the strain test of 4 parts of the casing.A new approach was proposed to extract knowledge rules from support vector clustering (SVC).Then SVC algorithm was adopted to get the clustering distribution matrix of the sample data with chosen features.Secondly,hyper-rectangle rules were constructed on the basis of the clustering distribution matrix.In order to make the rules more concise and explainable,hyper-rectangle rules should further simplified by using such means as rules merger,dimension reduction and interval extension.Finally,using the data mining method based on SVM,aero-engine rotor-stator rubbing positions diagnosis rules were extracted from a large number of rubbing position experiment data,then explanation and validated accordingly.The recognition rates were more than 99%,showing that the method is corrective and effective,and embodies great practical values.

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李爱,陈果,于明月.基于支持向量机的航空发动机转静碰摩部位诊断规则提取[J].航空动力学报,2013,28(10):2181~2193. LI Ai, CHEN Guo, YU Ming-yue. Aero-engine rotor-stator rubbing positions diagnosis rule acquisition based on support vector machine[J]. Journal Of Aerospace Power,2013,28(10):2181-2193.

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  • 收稿日期:2012-09-21
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  • 在线发布日期: 2013-10-24
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