计算机工程与设计
計算機工程與設計
계산궤공정여설계
COMPUTER ENGINEERING AND DESIGN
2015年
7期
1908-1911,1916
,共5页
发动机故障诊断%贝叶斯网络%可视化%参数学习%推理
髮動機故障診斷%貝葉斯網絡%可視化%參數學習%推理
발동궤고장진단%패협사망락%가시화%삼수학습%추리
engine fault diagnosis%Bayesian network%visualization%parameter learning%reasoning
在研究发动机各类故障诊断的基础上，结合贝叶斯网络从数据中学习的方法，提出一种能够根据实际样本数据对发动机的各类故障进行可视化诊断的方法，其充分考虑了先验知识，且能够根据实际样本数据对先验知识进行修正。以发动机W P7的故障为例，通过因果关系建立贝叶斯网络的可视化模型，结合先验知识进行参数学习和推理，实例结果表明，该模型及分析方法很好地反应了各部件或子系统的故障对于整个系统故障的影响以及各部件或子系统之间的依赖关系及依赖程度，有助于找出系统的薄弱环节和提高系统可靠性的途径。
在研究髮動機各類故障診斷的基礎上，結閤貝葉斯網絡從數據中學習的方法，提齣一種能夠根據實際樣本數據對髮動機的各類故障進行可視化診斷的方法，其充分攷慮瞭先驗知識，且能夠根據實際樣本數據對先驗知識進行脩正。以髮動機W P7的故障為例，通過因果關繫建立貝葉斯網絡的可視化模型，結閤先驗知識進行參數學習和推理，實例結果錶明，該模型及分析方法很好地反應瞭各部件或子繫統的故障對于整箇繫統故障的影響以及各部件或子繫統之間的依賴關繫及依賴程度，有助于找齣繫統的薄弱環節和提高繫統可靠性的途徑。
재연구발동궤각류고장진단적기출상，결합패협사망락종수거중학습적방법，제출일충능구근거실제양본수거대발동궤적각류고장진행가시화진단적방법，기충분고필료선험지식，차능구근거실제양본수거대선험지식진행수정。이발동궤W P7적고장위례，통과인과관계건립패협사망락적가시화모형，결합선험지식진행삼수학습화추리，실례결과표명，해모형급분석방법흔호지반응료각부건혹자계통적고장대우정개계통고장적영향이급각부건혹자계통지간적의뢰관계급의뢰정도，유조우조출계통적박약배절화제고계통가고성적도경。
Based on the study of various types of engine fault diagnosis ,combined with Bayesian network method of learning from the data ,a method that analyzed various engine fault visually according to the actual sample data was proposed .This method not only fully considers the prior knowledge ,but also corrects the prior knowledge according to the actual sample data .Finally , taking the engine WP7 fault as an example ,the visual Bayesian network model was modeled through the causality ,parameter learning and reasoning were performed combined with priori knowledge .The result shows that the model and analysis method can well reflect the failure of the parts or subsystems for the entire system failure and the dependencies and degree of that between the parts or subsystems ,which can help to find out the weak links of the system and the way to improve the reliability of the system .
