基于贝叶斯推理的包装件动力学模型优化选择研究

Kinetic Package Model Optimal Selection Using Bayesian Inference

目的在多种类型的模型中挑选出最优包装件模型,并实现参数识别的方法。方法文中将包装件模型构建为参数不确定模型,在贝叶斯推理的框架下,采用马尔可夫链蒙特卡洛法识别模型参数,采用偏差信息准则(DIC)计算各备选模型的DIC参数,选择出最优包装件模型。结果在振动实验台用质量块-缓冲材料模拟包装件并进行随机振动测试,分析结果表明,Bouc-Wen(n=2)模型为文中包装系统的最佳模型。结论文中提出的基于贝叶斯推理的包装件模型优化选择和参数识别方法考虑了模型不确定性,构建的模型可准确预测包装件在随机振动下加速度响应的时域信号。

The work aims to select the optimal model from various models and achieve parameter identification.The package model was constructed as the parameter uncertainty model.The model parameters were identified with the Markov Chain Monte Carlo method in Bayesian inference framework.Deviation information criterion(DIC)was used to calculate the DIC parameters of each alternative model and select the optimal package model.The mass block-buffer material was used to simulate the package and the random vibration test was carried out on the vibration test bench.The analysis result showed that the Bouc-Wen model(n=2)was the optimal model for the package.The model selection and parameter identification method proposed based on Bayesian inference takes the uncertainty of the model into account.The constructed model can accurately predict the time-domain signal of acceleration response of the package under random vibration.