基于贝叶斯网络的复合双轴转台精度推理研究

Research on precision inference of compound biaxial turntable based on Bayesian network

为精准预测复合双轴转台精度,分析了多因素对系统精度影响规律。利用复合双轴转台统计系统实验数据,学习构建以内轴精度、外轴精度、自准直精度和待检编码器精度为主要节点的贝叶斯网络结构;在Netica中建立系统精度推理模型,并通过证据敏感性分析和平均绝对误差(MAE)分析验证贝叶斯网络(BN)模型的有效性;运用自学习贝叶斯网络的概率推理,分析主要目标节点各变量的后验概率变化,对系统精度变化规律进行原因诊断和支持解释。研究结果表明:复合双轴转台精度自学习BN模型能够实现系统精度准确推理预测,系统精度超差的MAE值基本稳定在5%以内,且角度间隔0.125°和时间间隔20 s为系统最优控制参数,为贝叶斯网络技术在复合双轴转台精度推理中的应用提供了参考。

In order to accurately predict the accuracy of the compound biaxial turntable,the influencing rule of multiple factors on the system accuracy was analyzed.By using the statistical experimental data of the compound biaxial turntable,the system learns and constructs the Bayesian network structure with the inner axis accuracy,outer axis accuracy,auto-collimation accuracy and the accuracy of the encoder to be tested as the main nodes.The inference model of the system detection accuracy was established in Netica software,and the validity of the Bayesian network(BN)model was verified through evidence sensitivity analysis and mean absolute error(MAE)analysis.The self-learning Bayesian network probabilistic inference is used to analyze the posterior probability changes of each variable of the main target node,and to diagnose and support the reason for the change of the system accuracy.The research results show that the self-learning BN model with compound biaxial turntable accuracy can achieve accurate inference and prediction with system accuracy.And the MAE value of the system accuracy out of tolerance is basically stable within 5%,and the angular interval of 0.125°and the time interval of 20 s are the optimal control parameters of the system,which provides a reference for the application of Bayesian network technology in the accuracy inference of the compound biaxial turntable.