基于KPCA-LVQ和内置传感器信息采集的滚珠丝杠故障诊断研究  

Study in Fault Diagnosis of Ball Screw Based on KPCA-LVQ and Acquisition of Built-in Sensors Information

作　　者：俞昆 谭继文[1] 李善[1] 战红[1] 

机构地区：[1]青岛理工大学机械工程学院,山东青岛266525

出　　处：《机床与液压》2016年第21期155-158,共4页Machine Tool & Hydraulics

基　　金：国家自然科学基金资助项目(51075220);山东省高等学校科技计划项目(J13LB11);高等学校博士学科点专项科研基金资助项目(20123721110001);青岛市科技计划基础研究项目(12-1-4-4-(3)-JCH)

摘　　要：研究了全闭环数控机床伺服进给系统的编码器、光栅尺等内置传感器信息采集并从中获取滚珠丝杠故障状态信息的方法;在分析了滚珠丝杠信号的非线性、非平稳性特征的基础上,提出了基于小波包分解提取滚珠丝杠故障状态信号能量特征值的方法,并用该能量特征值与峰度、频率、方差等时-频特征量组成滚珠丝杠故障诊断的原始特征集,采用KPCA法剔除了对故障诊断贡献率不明显的冗余特征;建立了基于KPCA-LVQ神经网络的滚珠丝杠故障模型;并通过试验,对KPCA-LVQ与KPCA-BP两种神经网络的诊断结果进行了对比分析。证明了文中所研究方法对滚珠丝杠故障诊断的可行性和有效性。The method was studied of acquisition of internal information realized by encoder and grating ruler which built in servo feed system of whole closed-loop computer numbercial control （CNC） system of machine tool. On basis of analyzing characteristics of nonlinearity and non-stability of signal of the ball screw, a method was proposed of the energy eigenvalue extracted based on wavelet packets decomposing of fault state signal of the ball screw, and the energy eigenvalue with peak, frequency and square diference of the characteristic parameter of time-frequency domain were used to form the original characteristic set for fault diagnosis of the ball screw. Due to the redundancy of the original characteristic set, kernel principal component analysis （KPCA） was used to get rid of the characteristic parameter which was not obviously reflected the contribution rate of characteristics set in fault diagnosis. Fault model of the ball screw was established based on KPCA-LVQ neural nework （NN）. The test result＇s comparison between KPCA-LVQ and KPCA-BP of the two NN is analyzed, which illustrates that the studied method is effective and feasible in the application of fault diagnosis of ball

关 键 词：内置传感器信息采集 滚珠丝杠 KPCA-LVQ KPCA-BP 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化]