形态滤波优化算法用于滚动轴承故障诊断  

Fault Diagnosis Method of Rolling Bearings Based on Optimized Morphological Filter Algorithm

作　　者：宋平岗[1] 周军[1] 陈健亨[1] 

机构地区：[1]华东交通大学电气与电子工程学院,南昌330013

出　　处：《振动．测试与诊断》2013年第5期756-762,908,共7页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(50577025)

摘　　要：为了有效地从非线性、非平稳性的滚动轴承振动信号中提取有用的信息成分,提出了一种优化的形态滤波算法-Elman神经网络相结合的方法。首先,采用局域均值分解(LMD)将轴承振动信号分解成若干PF(product runction,简称PF)分量之和;然后,利用峭度最大准则选取PF分量,再用自适应多尺寸多结构元素形态滤波器对其进行滤波解调,进而提取出能量特征向量,作为Elman神经网络的输入参数;最后,区分滚动轴承故障状态和故障类型。仿真分析和试验研究表明,该方法能够有效地提取出滚动轴承的故障特征,与传统的高频共振解调方法相比效果更加明显,与小波包分析-BP神经网络故障诊断方法对比,显示出其具有更高的识别率,更加表明其可行性和有效性。To effectively extract useful information from nonlinear and non-stationary vibration signals of rolling bearings,a method is presented by combining an optimized morphological filter algorithm with Elman Neural Network.First,some product function(PF)components from vibration signals of the rolling bearing are obtained by local mean decomposition(LMD).Then,the PF component with the biggest kurtosis value is selected according to the kurtosis value criterion,and it is demodulated by adaptive morphology filter with multi-size and structure element.Thus,the energy feature vector is extracted as the input parameters of the Elman Neural Network.At last,the fault status and type of the rolling bearing are confirmed.The simulation analysis and experimental study show that this method could effectively extract fault features of the rolling bearing,more pronounced compared with the traditional method of high-frequency resonance demodulation.In addition,compared with Wavelet Packet Analysis-BP Neural Network Fault Diagnosis,this method is more feasible and effective with a higher recognition rate.

关 键 词：局域均值分解 形态滤波 ELMAN神经网络 滚动轴承 故障诊断 

分 类 号：TG156[金属学及工艺—热处理] TP206[金属学及工艺—金属学]