基于TWSVM算法的发动机故障识别方法
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摘要
为了快速有效地诊断出汽油发动机故障,提出了一种基于孪生支持向量机(TWSVM)的发动机故障诊断方法.该方法利用HC、CO、CO2、O2和NOx共5种尾气参数值,并对其进行规范化处理,然后把这些数据作为特征向量,用于孪生支持向量机构成的多分类器中进行训练和测试,从而达到识别故障类别的目的.试验结果表明:采用孪生支持向量机分类方法比利用传统支持向量机具有更好的分类效果,且训练速度更快;在小样本数据情况下,故障诊断正确率可达到98.4%,能有效描述汽车尾气成分变化与发动机故障状态之间的复杂关系.
In order to diagnose the failure of gasoline engine quickly and effectively,an engine fault diagnosis method based on twin support vector machine was proposed. The method is to use the five exhaust gas parameters such as HC,CO,CO2,O2,NOx,and normalize the data,extract the eigenvector as the learning sample,and then use the multi-classifier based on the twin support vector machine to train and test,so as to achieve the purpose of identifying the type of failure. The experimental results show that the classification method of twin support vector machine has stronger classification ability than that of the traditional support vector machine,and the training speed is faster. In the case of less samples data,the correctness of fault diagnosis can reach 98.4%. This can effectively describe the complicated relationship between the car exhaust composition changes and the engine fault state.
In order to diagnose the failure of gasoline engine quickly and effectively,an engine fault diagnosis method based on twin support vector machine was proposed. The method is to use the five exhaust gas parameters such as HC,CO,CO2,O2,NOx,and normalize the data,extract the eigenvector as the learning sample,and then use the multi-classifier based on the twin support vector machine to train and test,so as to achieve the purpose of identifying the type of failure. The experimental results show that the classification method of twin support vector machine has stronger classification ability than that of the traditional support vector machine,and the training speed is faster. In the case of less samples data,the correctness of fault diagnosis can reach 98.4%. This can effectively describe the complicated relationship between the car exhaust composition changes and the engine fault state.
引文
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[14]于俊钊.孪生支持向量机及其优化方法研究[D].北京:中国矿业大学计算机科学与技术学院,2014.
[15]Shao Y H,Chen W J,Wang Z,et al.Weighted linear loss twin support vector machine for large-scale classification[J].Knowledge-Based Systems,2015,73(1):276-288.
[16]Tomar D,Agarwal S.Multi-class twin support vector machine for pattern classification[C]//Proceedings of3rd International Conference on Advanced Computing,Networking and Informatics,Springer India,2016:97-110.
[17]孙逊.基于BP神经网络的电控发动机故障诊断研究[D].北京:北京林业大学车辆工程学院,2012.
[2]田径,刘忠长,许允.柴油机瞬变过程烟度排放的劣变分析[J].内燃机学报,2016,34(2):125-134.
[3]柳长源.相关向量机多分类算法的研究与应用[D].哈尔滨:哈尔滨工程大学信息与通信工程学院,2013.
[4]徐亚丹,王俊.基于BP人工神经网络和尾气分析的汽车故障诊断方法研究[J].农业装备与车辆工程,2008,208(11):15-17.
[5]李增芳,金春华,何勇.基于废气成分分析和支持向量机的发动机故障诊断[J].农业工程学报,2010,26(4):143-146.
[6]邱建坤.基于孪生支持向量机的特征选择与多类分类算法研究[D].秦皇岛:燕山大学电气工程学院,2015.
[7]Jayadeva,Khemchandani R,Chandra S.Twin support vector machines for pattern classification[J].IEEETransactions on Pattern Analysis&Machine Intelligence,2007,29(5):905-910.
[8]Qi Z,Tian Y,Shi Y.Robust twin support vector machine for pattern classification[J].Pattern Recognition,2013,46(1):305-316.
[9]徐凯.孪生支持向量机在滚动轴承振动故障诊断中的应用[J].煤矿机械,2016,37(4):147-150.
[10]王立国,杜心平.K均值聚类和孪生支持向量机相结合的高光谱图像半监督分类[J].应用科技,2017,44(3):1-7.
[11]Li D,Tian Y,Xu H.Deep twin support vector machine[J].IEEE International Conference on Data Mining Workshop,2015,29(1):65-73.
[12]Xu Y.Maximum margin of twin spheres support vector machine for imbalanced data classification[J].IEEETransactions on Cybernetics,2016,47(6):1540-1550.
[13]丁世飞,张建,张谢锴,等.多分类孪生支持向量机研究进展[J].软件学报,2014,10(11):65-73.
[14]于俊钊.孪生支持向量机及其优化方法研究[D].北京:中国矿业大学计算机科学与技术学院,2014.
[15]Shao Y H,Chen W J,Wang Z,et al.Weighted linear loss twin support vector machine for large-scale classification[J].Knowledge-Based Systems,2015,73(1):276-288.
[16]Tomar D,Agarwal S.Multi-class twin support vector machine for pattern classification[C]//Proceedings of3rd International Conference on Advanced Computing,Networking and Informatics,Springer India,2016:97-110.
[17]孙逊.基于BP神经网络的电控发动机故障诊断研究[D].北京:北京林业大学车辆工程学院,2012.