摘要
支持矢量机(Support vector machine,SVM)有比神经网络更强的泛化能力,且能保证找到的极值解就是全局最优解,同时它还较好地解决了小样本的学习分类问题。针对齿轮振动信号的非平稳特征和现实中难以获得大量故障样本的实际情况,提出了一种基于经验模态分解(Empirical mode decomposition,EMD)和支持矢量机的齿轮故障诊断方法。首先对原始信号进行经验模态分解,将其分解为多个平稳的固有模态函数(Intrinsic mode function,IMF)之和,然后对每一个IMF分量建立AR模型,最后提取模型的自回归参数和残差的方差作为故障特征矢量,并以此作为SVM分类器的输入参数来识别齿轮的工作状态和故障类型。试验结果表明,在小样本情况下仍能准确、有效地对齿轮的工作状态和故障类型进行分类。
Support sector machine (SVM) has stronger generalization ability than artificial neural networks and can guarantee that the local optimal solution is exactly the global optimal one. Meanwhile, it can solve the learning problem of smaller number of samples. According to the situation that it is hard to obtain enough fault samples and the non-stationary characteristics of gears fault vibration signals, a gears fault diagnosis method based on empirical mode decomposition (EMD) and SVM is proposed. Firstly, vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs), then the AR model of each IMF component is established, finally, the auto-regressive parameters and the variance of remnant are regarded as the fault characteristic vectors and served as input parameter of SVM classifier to classify working condition of gears. The experimental results show that the proposed approach can classify working condition of gears accurately and effectively even in the case of smaller number of samples.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2005年第1期140-144,共5页
Journal of Mechanical Engineering
基金
国家自然科学基金(50275050)高等学校博士点专项科研基金(20020532024)资助项目。
