液体火箭发动机的分层贝叶斯变分推理故障诊断方法

Fault Diagnosis of Liquid Rocket Engine Based on Hierarchical Bayesian Network Variational Inference

针对稀疏数据场景下,传统的多项式-狄利克雷模型存在一定的分类精度问题,提出一种基于变分推理的分层贝叶斯网络的参数估计方法.通过在传统的多项式-狄利克雷模型中引入超先验,构建出的分层多项式-狄利克雷模型可用于贝叶斯网络中的条件分布估计.对分层多项式-狄利克雷模型的先验依赖结构进行分析研究,提出一种快速准确的自组织变分推理算法.与传统的分类模型相比,本文提出的分层多项式-狄利克雷模型在处理小数据集液体火箭发动机的故障分类中有显著的性能提高.

In order to improve classification accuracy of traditional multinomial-Dirichlet model in sparse data scenario,a hierarchical Bayesian network parameter estimation method was proposed based on variational inference.Introducing a hyper-prior into the traditional multinomial-Dirichlet model,the hierarchical multinomial-Dirichlet model was constructed to estimate the conditional distribution in Bayesian networks.Analyzing the prior dependency structure of hierarchical multinomial-Dirichlet model,a fast and accurate self-organizing variational reasoning algorithm was developed.Compared with the traditional classification model,the hierarchical multinomial-Dirichlet model proposed in this paper shows a significant performance improvement in dealing with the fault classification problem of liquid rocket engines with small data sets.