基于多重分形与SVM的齿轮箱故障诊断研究  

Study on Gearbox Fault Diagnosis based on Multi-fractal and SVM

作　　者：朱云博[1,2] 冯广斌 孙华刚 李顺德[2] 

机构地区：[1]军械技术研究所,河北石家庄050003 [2]军械工程学院,河北石家庄050003

出　　处：《机械传动》2012年第6期99-102,共4页Journal of Mechanical Transmission

摘　　要：针对齿轮箱振动信号的非平稳性和非线性,提出一种多重分形和支持向量机相结合的故障诊断方法。运用多重分形理论方法对齿轮振动信号进行分析,通过分析发现多重分形谱和广义维数作为故障特征能够很好地反映齿轮箱的工作状态;对支持向量机的参数利用粒子群优化算法进行优化,并将齿轮箱振动信号的多重分形特征量作为支持向量机的输入参数以识别齿轮的故障类型。实验结果表明,该方法在样本较小的情况下能够准确对齿轮箱的故障类型进行分类。Aiming at that the gearbox vibration signals are nonlinear and non-stationary,a fault diagnosis method based on the theory of multi-fractal and support vector machine（SVM） is proposed.First the multi-fractal theory are applied to analyze gearbox vibration signals,the analysis results show that multi-fractal spectrum and general dimension give a good presentation for gearbox working condition.Then particle swarm optimization is applied to optimize the parameter of support vector machine.The multi-fractal characteristic parameters of gearbox vibration signals are regarded as the fault characteristic vectors and served as input parameters of SVM classifier to classify the fault types of the gearbox.The experimental results show that in the case of small number of samples,this method can classify the different fault types of gearbox accurately.

关 键 词：多重分形 支持向量机 故障诊断 多重分形谱 广义维数 粒子群优化算法 

分 类 号：TH132.41[机械工程—机械制造及自动化] TH165.3