基于Hilbert边际谱和IPSO-SVDD的滚动轴承故障诊断  

Rolling bearing fault diagnosis based on Hilbert marginal spectrum and IPSO-SVDD

作　　者：高峰[1] 申江江 曲建岭[1] 袁涛[1] 何勰 余路[1] Gao Feng Shen Jiangjiang Qu Jianhng Yuan Tao He Xie Yu Lu(Qingdao Branch of Naval Aeronautical Engineering Institute, Qingdao 266041, China Aeronautical Training Center of Naval Aeronautical Engineering Institute, Qingdao 266109, China Unit 91181 of PLA, Qingdao 266400 ,China)

机构地区：[1]海军航空工程学院青岛校区 [2]海军航空工程学院航空训练基地 [3]解放军91181部队

出　　处：《电子测量与仪器学报》2017年第6期892-898,共7页Journal of Electronic Measurement and Instrumentation

摘　　要：滚动轴承是旋转机械状态监控及故障诊断的重要研究内容。为了更加高效的对轴承故障位置及故障程度进行诊断,提出了一种基于Hilbert边际谱和改进粒子群算法(IPSO)优化支持向量数据描述(SVDD)相结合的滚动轴承故障诊断方法。该方法首先求取轴承振动信号的本征模态函数,在此基础上得到信号的边际谱以及信号的AR模型参数,积分求取边际谱的能量特征函数和AR模型参数相结合构成系统特征向量。然后针对传统网格搜索法或凭经验确定SVDD核心参数的缺点,提出利用基于动态因子的粒子群算法对SVDD的核心参数惩罚常数C及核函数宽度σ进行优化,利用优化后的SVDD模型对滚动轴承各状态信号进行智能诊断。人工数据集及真实数据集实验结果表明,该方法可以有效识别各故障状态信号,并且优化后模型的诊断效率及诊断精度高于传统网格搜索法确定的模型。Rolling bearing is significant research content for rolling machine condition monitoring and fault diagnosis. In order to diagnose the rolling bearing fault position and degree more effectively,a rolling bearing fault diagnosis method based on Hilbert marginal spectrum and support vector data description（ SVDD） optimized by improved particle swarm optimization（ IPSO） is proposed. In this method,the rolling bearing vibration signal is decomposed into a set of intrinsic mode functions（ IMFs）,then marginal spectrum and autoregressive（ AR） model parameters are established and system feature vector is constructed of AR parameters and feature power function,which is obtained from marginal spectrum. In order to solve the problem of deciding SVDD＇s significant parameters by traditional gridsearching or experience,a method using dynamic factor based particle swarm algorithm is used to find the optimized SVDD＇s significant parameters penalty constant C and kernel function width σ,and the optimized model is put into use of intelligent rolling bearing fault diagnosis. The experiment results of manual and real data sets show that different kinds of rolling bearing fault conditions can be recognized effectively by the proposed method with higher efficiency and precision than traditional grid-searching method.

关 键 词：经验模态分解 边际谱 AR模型 粒子群算法 支持向量数据描述 

分 类 号：TN911[电子电信—通信与信息系统] TH165[电子电信—信息与通信工程]