基于PCA和SVM的内燃机故障诊断

为有效对内燃机运行状态进行评估,根据内燃机振动信号特征和故障样本较少的特点,提出了基于主分量分析和支持向量机进行内燃机状态判别的故障诊断方法。提取内燃机振动特征参数,利用主分量分析消除其信息冗余,提取反映内燃机运行状态的主分量特征,实现内燃机振动特征参数降维。通过选择适合内燃机振动信号的径向基核函数,构造一对多的支持向量机多类分类器,对主分量特征进行训练学习,实现内燃机运行状态判别。通过对模拟内燃机不同运行状态的试验分析,结果表明该方法可以有效识别内燃机不同的运行状态。

Fault Diagnosis of Internal Combustion Engine Using PCA and SVM

Internal combustion engine(ICE) is a complex mechanical system.It is difficult to identify ICE health status for lack of fault data and its complex vibration characteristics.In order to effectively evaluate the health status of ICE,a fault diagnosis method based on principal component analysis(PCA) and support vector machine(SVM) is investigated.Firstly,principal component features of ICE are extracted through eliminating redundancy and reducing the dimension of original vibration signal feature parameters by PCA.Then,these features are taken as training samples and one-against-all SVM classifier is designed to identify health status of ICE by using radial basis kernel function.Through analyzing the vibration features of ICE under different conditions,experimental results indicate that the fault diagnosis method can effectively recognize different status of ICE.