基于KP CA-LVQ和内置传感器信息采集的滚珠丝杠故障诊断研究

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KP-CA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 charac-teristic 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 screw.