基于相似證據(jù)理論的FTA模型研究與應用
發(fā)布時間:2018-09-03 20:12
【摘要】:事故樹分析方法是安全評價中廣泛應用的方法,但是,如何評估基本事件概率發(fā)生范圍一直是定量分析上的難點,傳統(tǒng)事故樹分析過分依賴統(tǒng)計數(shù)據(jù),也制約著事故樹分析方法的發(fā)展。 為了對事故樹分析中的多位專家意見信息進行有效融合,本文提出一種基于相似度分析的事故樹分析模型。首先,,引入數(shù)據(jù)融合中的相似性的概念,用來分析多個專家意見之間的相似度,并依據(jù)分析結(jié)果給每位專家的意見分配可信度權(quán)值。然后,利用證據(jù)理論對專家意見進行合成,預測系統(tǒng)事故發(fā)生概率,并對引起頂事件發(fā)生的基本事件的重要度進行判定。最后,建立針對某鋼鐵集團煤氣柜爆炸的事故樹定量分析模型,得出頂事件發(fā)生概率,并找出導致事故發(fā)生的最危險路徑,為管理者制定有針對性的措施提供參考依據(jù)。 在本文的結(jié)尾處對全文的研究工作進行了總結(jié),對未來的研究方向提出了工作重心,并且指出了本文所編制的煤氣柜爆炸事故樹有待改進的地方,以及證據(jù)理論算法的深入研究的必要性。最后本文對未來在證據(jù)理論算法和事故樹定量分析結(jié)合的研究方向進行了展望。
[Abstract]:Accident tree analysis is a widely used method in safety evaluation. However, how to evaluate the probability range of basic events is always a difficult point in quantitative analysis. Traditional accident tree analysis relies too much on statistical data. It also restricts the development of accident tree analysis method. In order to effectively fuse the multi-expert opinion information in accident tree analysis, a fault tree analysis model based on similarity analysis is proposed in this paper. Firstly, the concept of similarity in data fusion is introduced to analyze the similarity between several expert opinions, and each expert's opinion is assigned credibility weight according to the analysis results. Then, the expert opinion is synthesized by using the evidence theory to predict the probability of the system accident, and the importance of the basic event that causes the top event is determined. Finally, a quantitative analysis model of accident tree for explosion of gas tank of a certain iron and steel group is established, and the probability of top event is obtained, and the most dangerous path leading to the accident is found out, which provides a reference for managers to formulate targeted measures. At the end of this paper, the research work of the full text is summarized, the focus of the future research is put forward, and the place that the gas tank explosion accident tree compiled in this paper needs to be improved is pointed out. And the necessity of further research on evidence theory algorithm. Finally, the research direction of combining evidence theory algorithm and accident tree quantitative analysis in the future is prospected.
【學位授予單位】:武漢科技大學
【學位級別】:碩士
【學位授予年份】:2014
【分類號】:TQ086;TQ547.91
本文編號:2221049
[Abstract]:Accident tree analysis is a widely used method in safety evaluation. However, how to evaluate the probability range of basic events is always a difficult point in quantitative analysis. Traditional accident tree analysis relies too much on statistical data. It also restricts the development of accident tree analysis method. In order to effectively fuse the multi-expert opinion information in accident tree analysis, a fault tree analysis model based on similarity analysis is proposed in this paper. Firstly, the concept of similarity in data fusion is introduced to analyze the similarity between several expert opinions, and each expert's opinion is assigned credibility weight according to the analysis results. Then, the expert opinion is synthesized by using the evidence theory to predict the probability of the system accident, and the importance of the basic event that causes the top event is determined. Finally, a quantitative analysis model of accident tree for explosion of gas tank of a certain iron and steel group is established, and the probability of top event is obtained, and the most dangerous path leading to the accident is found out, which provides a reference for managers to formulate targeted measures. At the end of this paper, the research work of the full text is summarized, the focus of the future research is put forward, and the place that the gas tank explosion accident tree compiled in this paper needs to be improved is pointed out. And the necessity of further research on evidence theory algorithm. Finally, the research direction of combining evidence theory algorithm and accident tree quantitative analysis in the future is prospected.
【學位授予單位】:武漢科技大學
【學位級別】:碩士
【學位授予年份】:2014
【分類號】:TQ086;TQ547.91
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