基于EMD和非线性峭度的齿轮故障诊断  

Fault Diagnosis of Gear Based on Empirical Mode Decomposition and Nonlinear Kurtosis

作　　者：张德祥[1] 汪萍[2] 吴小培[1] 高清维[1] 

机构地区：[1]安徽大学计算智能与信号处理教育部重点实验室,合肥230039 [2]安徽建筑工业学院机械与电气工程学院,合肥230601

出　　处：《振动．测试与诊断》2012年第1期56-61,161,共6页Journal of Vibration,Measurement & Diagnosis

基　　金：安徽省教育厅自然科学基金资助项目(编号:KJ2011A013);国家自然科学基金资助项目(编号:60872163)

摘　　要：采用经验模式分解(empirical mode decomposition,简称EMD)和非线性峭度的统计特性对振动加速度传感器获取的齿轮箱振动响应信号进行特性分析。利用EMD分解获得振动响应信号的本征模式函数,用非线性Tea-ger能量算子计算每个本征模式函数的瞬时能量,并对本征模式函数进行系数的非线性峭度计算,提取系统的特征信息。仿真结果表明,用经验模式分解和非线性峭度可实现在线监测齿轮运转工作状态,及时发现齿轮的早期故障,提高了故障检测的可靠性。Characteristics analysis of gearbox vibration response signals captured from vibrating acceleration sensor based on empirical mode decomposition(EMD) and statistical properties of nonlinear kurtosis is proposed.The vibration response signal is firstly decomposed into intrinsic mode function(IMF) by the empirical mode decomposition method.Then nonlinear Teager energy operator tracks the modulation energy of each IMF.The desired feature of statistical properties of vibration signals can be extracted from the coefficient nonlinear kurtosis value of intrinsic mode function.It is significant for the mechanical operation security to do some research on how to monitor operating state of gear and detect incipient faults as soon as possible.Experiment results have shown the feasibility and efficiency of the EMD and nonlinear kurtosis method in fault message diagnosis,and additionally,the algorithm is reliable to be implemented with fault detection.

关 键 词：齿轮箱振动响应信号 经验模式分解 本征模式函数 非线性峭度 故障诊断 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置] TH132.41[自动化与计算机技术—控制科学与工程]