基于LSTM-GRU神经网络的机床主轴回转误差分离降噪研究  

Research on the Separation and Noise Reduction Method of Spindle Radial Rotation Error Based on LSTM-GRU Neural Network

作　　者：迟玉伦[1] 李希铭 朱文博[1] 余建华 CHI Yulun;LI Ximing;ZHU Wenbo;YU Jianhua(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学机械工程学院,上海200093

出　　处：《计量学报》2024年第11期1615-1625,共11页Acta Metrologica Sinica

基　　金：国家自然科学基金(51605294)。

摘　　要：频域三点法是分离主轴回转误差的常用方法,其误差分离精度受被测信号中噪声的影响较大,不适当的降噪方法会使测试结果失真。为此,提出了基于LSTM-GRU神经网络的机床主轴回转误差分离降噪方法。首先,使用经遗传算法优化的传感器夹角搭建测试系统并对主轴回转误差进行数据信号采集。然后,配置卡尔曼滤波器对3个传感器信号进行降噪,通过三点法分离出同步误差和异步误差。最后,使用LSTM-GRU模型分别对同步误差和异步误差降噪,并将该模型降噪结果与LSTM-LSTM双层神经网络降噪、卡尔曼滤波和小波阈值降噪和结果对比,分别计算其Allan方差来评价不同方法的降噪效果。实验结果显示,使用该LSTM-GRU模型降噪后的同步误差Allan方差为2.014×10^(-8)mm^(2),异步误差Allan方差为3.967×10^(-8)mm^(2),均小于卡尔曼滤波、小波阈值降噪和LSTM-LSTM双层神经网络降噪结果。LSTM-GRU模型的降噪效果最优,被测主轴在转速为6000 r/min时的同步误差为2.42μm,异步误差为3.21μm,符合实际情况。The spindle rotation error is one of the important indicators to measure the accuracy of CNC machine tools.The three-probe method is a common method for separation of spindle rotation error.The error separation accuracy is greatly affected by the noise in the testing signal,the spindle rotation error is close to the noise level of the test system.Inappropriate noise reduction methods can distort the test results.The frequency domain three-probe method is a common method for separating spindle rotation errors.Its error separation accuracy is greatly affected by the noise in the measured signal.Inappropriate noise reduction methods will distort the test results.To this end,a spindle rotation error separation and noise reduction method based on LSTM-GRU neural network is proposed.First,a test system is built using the optimized sensor angle by genetic algorithms and the spindle rotation error signal is acquired.Then,the Kalman filter is configured to reduce the noise of the three sensor signals,the synchronous rotation error and asynchronous rotation error are separated by the three-probe method in frequency domain.Finally,the LSTM-GRU model is used to reduce the noise of synchronous and asynchronous rotation error respectively.The noise reduction results of the LSTM-GRU model are compared with the results of LSTM-LSTM model、Kalman filtering and Wavelet threshold denoise methods.The Allan variance is calculated to evaluate the noise reduction effect of different methods.The experimental result shows that after noise reduction using the LSTM-GRU model,the Allan variance of the synchronous rotation error is 2.014×10^(-8) mm^(2) and the Allan variance of the asynchronous rotation error is 3.967×10^(-8) mm^(2),which are both less than the results of Kalman filtering and Wavelet threshold noise reduction.The noise reduction effect of the LSTM-GRU model is optimal.The asynchronous rotation error of the spindle at the test speed of 6000 r/min is 2.42μm and the asynchronous rotation error is 3.21μm,which meets the act

关 键 词：几何量计量 主轴回转误差 频域三点法 LSTM-GRU 传感器夹角优化 

分 类 号：TB92[一般工业技术—计量学]