使用BP神经网络模型研究逆向卸荷膜片式减压器的稳定性能

采用数据挖掘中BP(back propagation)神经网络模型来研究逆向卸荷膜片式减压器的结构参数与稳定性能之间的依赖关系,得到结构参数变化,尤其是多结构参数耦合变化下减压器的稳定性结果.其中稳定性对阻尼孔直径、膜片刚度非常敏感,对弹性元件材料的阻尼系数、低压腔有效长度较为灵敏.由此提出减弱振荡的各种措施:增大阻尼孔直径、增大膜片刚度、在一定范围(标准值的6.5倍)内增大弹性元件材料的阻尼系数、增大低压腔有效长度、减小阀芯质量.数值实验误差分析表明:该模型不存在过拟合、局部最优的情况,其预测结果是可靠的,可为减压器的设计和系统分析提供决策支持.而且,该模型对不同类型的数据集具有通用性,可以用来研究其他部件的结构参数与性能指标的依赖关系.

Research on stability of reverse unloading diaphragm pressure reducing regulator using BP neural network model

Back propagation(BP) neural network model was used to study the dependency between the structural parameters and the stability of reverse unloading diaphragm pressure reducing regulator(PRR).The stability of PRR obtained by adjusting structural parameters,in particular,multi-structure parameters,proves that the stability of PRR is very sensitive to the diameter of damping orifice and the stiffness of diaphragm, and also relatively sensitive to the damping coefficient of springy elements material and the effective length of the low-pressure chamber.Thus,various measures for improving the dynamic stability were proposed, including increasing the diameter of damping orifice,the stiffness of diaphragm,the damping coefficient of springy elements material within a certain range(6.5 times of standard value), the effective length of the low-pressure chamber and reducing the mass of valve spool.The BP neural network model tested by the error analysis of the numerical experiments does not show any phenomenon of over-fitting and local optimum.The predictions of the model are reliable,which can be used to support the decision for the design of the PRR and system analysis.In addition,the model is applicable for different data sets,and can be used to study the dependency between the structural parameters and the performance of other components.