基于最大Lyapunov指数的数控机床精度状态识别方法

摘要

提出一种基于最大Lyapunov指数的数控机床精度状态识别方法。通过数控机床在XOY平面四种不同圆周进给速度下的圆度误差数据产生一维时间序列，依据小波方法对时间序列降噪后采用C-C方法计算得到时间延迟、嵌入维数等混沌特性参数，对数控机床进行混沌相空间重构。求取Wolf方法下的最大Lyapunov指数，结合功率谱图对比分析发现机床系统具有混沌特性，且随着圆周进给速度的增加，最大Lyapunov指数减小；并通过实验测试及分析验证了这一结论。%The method for recognition of CNC machine tool precision state was proposed based on the lar-gest lyapunov exponent. One-dimensional time series is generated by the roundness error data in the XOY plane of four kinds of different circular feed rate. The wavelet analysis is adopted to denoise the time series. The chaotic characteristic parameters, such as time delay, embedding dimension, are obtained based on the C-C method, and then the chaotic phase space of CNC machine tool is reconstructed. The Wolf method is used to calculate the largest Lyapunov exponent, and with the power spectrum picture, the chaotic character-istic of the machine tool system is found. With the increase of circular feed rate, the largest Lyapunov expo-nent decreases. This conclusion is verified by experiment test and analysis.