基于内禀模态奇异值分解和支持向量机的故障诊断方法  

Fault Diagnosis Approach Based on Intrinsic Mode Singular Value Decomposition and Support Vector Machines

作　　者：程军圣[1] 于德介[1] 杨宇[1] 

机构地区：[1]湖南大学机械与汽车工程学院,长沙410082

出　　处：《自动化学报》2006年第3期475-480,共6页Acta Automatica Sinica

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

摘　　要：提出了一种基于内禀模态(Intrinsic mode functions,简称IMFs)奇异值分解和支持向量机(Support vector machine,简称SVM)的故障诊断方法．采用经验模态分解(Empirical mode decomposition,简称EMD)方法对旋转机械故障振动信号进行分解,将得到的若干个内禀模态分量自动形成初始特征向量矩阵,然后对该矩阵进行奇异值分解,提取其奇异值作为故障特征向量,并进一步根据支持向量机分类器的输出结果来判断旋转机械的工作状态和故障类型．对齿轮振动信号的分析结果表明,即使在小样本情况下,基于内禀模态奇异值分解和支持向量机的故障诊断方法仍能有效地识别齿轮的工作状态和故障类型．A fault diagnosis approach based on intrinsic mode singular value decomposition and support vector machines is put forward. Firstly, the EMD method is used to decompose the rotating machinery vibration signals into a number of intrinsic mode functions by which the initial feature vector matrixes are formed automatically. Secondly, by applying the singular value decomposition technique to the initial feature vector matrixes, the singular values are obtained. Finally, the singular values serve as the fault characteristic vectors to be input to the support vector machine classifier and the work conditions and fault patterns are identified by the output of the classifier. The analysis results from gear vibration signals show that the fault diagnosis method based on intrinsic mode singular value decomposition and support vector machines can extract fault features effectively and classify working conditions and fault patterns of gears accurately even when the number of samples is small.

关 键 词：旋转机械 故障诊断 经验模态分解 内禀模态函数 奇异值分解 支持向量机 

分 类 号：TH17[机械工程—机械制造及自动化]