基于贝叶斯优化神经网络的螺栓松动特性预测

Influence factors prediction of thread looseness based on Bayesian optimization neural network

针对螺纹松动过程影响因子多且具有典型非线性特征,预紧力衰减难以准确预测的问题,提出了一种基于贝叶斯优化神经网络的螺栓防松性能预测方法。首先建立了螺纹松动的动力学模型,并采用响应曲面法定量分析了各因子对残余预紧力的影响,确定了初始预紧力和振幅为影响松脱最敏感的两个因子;进一步采用贝叶斯优化算法,建立基于神经网络的螺栓残余预紧力预测模型,实现螺栓残余预紧力的精准预测,并对该模型进行了验证。结果表明:相对于三层神经网络、高斯过程回归以及支持向量机模型等,基于贝叶斯优化的神经网络预测模型的均方误差最小,且R2系数最接近1,通过试验验证,螺栓残余预紧力预测值与实际测试值误差在7%之内,验证了模型的有效性及可靠性,为螺栓可靠性防松设计奠定基础。

In the process of thread loosening,there are many influencing factors that have typical nonlinear characteristics,and the attenuation of pre tightening force cannot be predicted.Aiming at these problems,a method for predicting the residual pre tightening force of bolts based on mechanism model and test data was proposed.A dynamic model of thread loosening was built,the response surface method was used to quantitatively analyze the influence of factors on the residual preload,and the initial preload and amplitude were determined as the two most sensitive factors affecting looseness.Further,Bayesian optimization algorithm was used to establish a prediction model of bolt residual preload based on neural network,which could accurately predict the bolt residual preload,and the model was verified.The results showed that the mean square error of the neural network prediction model based on Bayesian optimization was the smallest,and the R2 coefficient was the closest to 1,which was superior to the three-layer neural network,Gaussian process regression and support vector machine models.The experimental verification showed that the error between the predicted value of bolt residual preload and the actual test value was within 7%,which verified the effectiveness and reliability of the model.It laid a foundation for the reliability design of bolts.