摘要
数控车床主传动系统是机床的核心部件,其一旦发生故障会造成加工质量甚至作业安全问题。数字孪生技术能降低故障诊断的难度,但目前研究仍存在物理实体到虚拟实体转换效率低和神经网络过拟合问题。为了解决上述问题,提出一种基于数字孪生和正则化BP神经网络的故障诊断方法。建立数控车床主传动系统数字孪生模型,通过OPC UA通信完成了物理实体和虚拟实体间孪生数据的交换,对比分析正则化改善过拟合问题的4种方法,构建了丢弃法正则化BP神经网络故障诊断模型。通过对比不同信噪比下BP神经网络、丢弃法正则化BP神经网络和卷积神经网络的损失函数和预测准确度,验证了诊断模型的可行性和算法的适用性。
The main drive system of CNC lathe is the core component of the machine tool,and its failure can cause machining qual-ity and even operational safety problems.Digital twin technology can reduce the difficulty of fault diagnosis,but the current research still suffers from low efficiency of physical entity to virtual entity conversion and neural network overfitting problems.To solve the above prob-lems,a fault diagnosis method based on digital twin and regularized BP neural network was proposed.A digital twin model of CNC lathe main drive system was established,and the exchange of twin data between physical and virtual entities was completed through OPC UA communication.Four regularization methods to improve the overfitting problem were compared and analyzed,and a fault diagnosis model was constructed based on regularized BP neural network by drop out method.By comparing the loss functions and prediction accuracy of BP neural network,DropOut-BPNN and convolutional neural network under different signal-to-noise ratios,the feasibility of the diag-nostic model and the applicability of the algorithm are verified.
作者
梁迪
李又佳
李依明
吴金颖
LIANG Di;LI Youjia;LI Yiming;WU Jinying(School of Mechanical Engineering,Shenyang University,Shenyang Liaoning 110044,China)
出处
《机床与液压》
北大核心
2024年第10期215-220,共6页
Machine Tool & Hydraulics
