基于统一理论框架的故障诊断模型及学习进化方法  

Fault diagnosis models and evolution method based on a unified theory framework

作　　者：张士刚[1] 罗旭 骆彦廷[1] 杨拥民[1] ZHANG Shigang;LUO Xu;LUO Yanting;YANG Yongmin(Science and Technology on Integrated Logistics Support Laboratory,College of Intelligence Science and Technology,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学智能科学学院,装备综合保障技术重点实验室,湖南长沙410073

出　　处：《国防科技大学学报》2020年第2期142-149,共8页Journal of National University of Defense Technology

基　　金：国家自然科学基金资助项目(61903370,61503398)。

摘　　要：以概率图理论为基础,系统研究基于这一理论框架的故障诊断模型,对模型的构建方法以及在不同场景下的模型演化方案进行探讨,使得在统一理论框架下可实现多模式系统故障、耦合故障、动态故障、故障预测等复杂情形的诊断。为了弥补单独利用基于模型的方法和基于数据的方法的缺陷,研究了诊断模型的学习进化策略,实现了诊断效果的改进和优化。对模型后续的能力扩展和可能的研究方向进行了展望,为后续理论研究提供了参考。A unified diagnosis model based on probabilistic graphical theory was studied.The model constructing method and its variations in different scenarios were discussed.Complex problems such as fault diagnosis in multimode systems,diagnosis with coupling faults,dynamic faults and fault prognosis were solved by using the framework.In order to combine the advantages of the model-based method and the data-driven method,a model learning algorithm was proposed,by means of which the diagnostic result was improved.In the end,the possible model developing directions and research focus were discussed,which can provide a reference for the follow-up theory research.

关 键 词：贝叶斯网 概率图模型 故障诊断 机器学习 多信号模型 

分 类 号：TN95[电子电信—信号与信息处理]