基于PF能量特征和优化神经网络的轴承诊断  

Rolling Bearing Diagnosis Based on the PF Energy Characteristics and Optimization of Neural Network

作　　者：潘阳[1] 陈安华[1] 何宽芳[1] 李学军[1] 曾波[1] 

机构地区：[1]湖南科技大学机械设备健康维护湖南省重点实验室,湘潭411201

出　　处：《振动．测试与诊断》2013年第S1期120-124,224,共6页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51175169);国防预研基金资助项目;湖南省高等学校科学研究资助项目(11C0530);湖南科技大学研究生创新基金资助项目(S120015)

摘　　要：内圈点蚀、外圈压痕和滚动体磨损是滚动轴承常见典型故障,为实现其快速、准确诊断,提出基于振动信号局部均值分解(local mean decomposition,简称LMD)的PF分量能量特征和神经网络相结合的滚动轴承诊断方法。对振动信号进行局部均值分解,将其分解为若干个乘积函数(product function,简称PF)分量之和,以获得的PF分量能量特征作为神经网络输入进行滚动轴承的故障类型的识别,同时引入遗传算法对神经网络结构参数进行优化,提高故障识别诊断速度和准确率。结果表明,该方法用于轴承典型故障诊断有较高的诊断速率和故障识别率。Inner ring erosion, the outer indentation and rolling element wear are typical faults of rolling bearing. In order to diagnose these faults rapidly and accurately,the paper proposes a novel diagnosis method of rolling bearing based on the energy characteristics of PF component and neural network by the vibration signal of local mean decomposition(local mean decomposition, LMD). The vibration signal is decomposed into several PF components by the local mean decomposition, the calculated energy characteristics of the PF component are inputted to the neural network to identificate the type of rolling bearing faults. At the same time, the genetic algorithm is introducted to optimize the structure parameters of neural network, which improves diagnostic rate and accuracy of faults. The results show that this method has a higher diagnosis and recognition rate for the typical faults of rolling bearing.

关 键 词：滚动轴承 局部均值分解 遗传算法 神经网络 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]