發(fā)布時間：2018-04-09 09:32

  本文選題：石化復雜系統　切入點：故障診斷　出處：《燕山大學》2015年碩士論文

【摘要】：石油化工生產是社會發(fā)展的基礎產業(yè)和經濟發(fā)展的支柱產業(yè),它在現代化社會發(fā)展進程中起著十分重要的作用。隨著石油化工過程的生產結構和規(guī)模日趨現代化、大型化和復雜化,生產事故發(fā)生的概率也逐漸增加。因而對石化生產過程進行有效的故障診斷來預防或避免事故的發(fā)生勢在必行。為了進行有效的故障診斷,本文提出了混合故障診斷方法,其包括故障監(jiān)測和故障診斷兩個方面,以某石化公司的氣體分餾裝置中的脫異丁烷單元為應用實例進行研究驗證。首先建立了基于主元分析(Principal Component Analysis,PCA)的故障監(jiān)測模型。運用主元分析方法對設置的4種在線工況進行了故障監(jiān)測,監(jiān)測結果均與預先設置的工況一致,結果表明運用主元分析方法不僅可以極大的降低數據維數,簡化計算,而且可以有效地進行在線監(jiān)測,及時發(fā)現故障。然后建立了基于誤差反饋神經(Back Propagation,BP)網絡的診斷模型。結果表明運用BP神經網絡構建故障診斷系統,只能判斷出故障所屬的故障類別,無法判斷出具體的故障情況。采用DS(Dempster-Shafer evidence theory)證據理論對BP神經網絡的診斷結果進行數據融合,結果表明運用DS證據理論可以在預知工況所屬故障類別的前提下有效地診斷出故障的原因。但是BP-DS相結合的方法仍存在著診斷時間長,計算量大等不足之處。徑向基(Radial Basis Function,RBF)神經網絡雖然沒有BP神經網絡應用廣泛,但是其分類能力、逼近能力、訓練速度等特性全都優(yōu)于BP網絡,于是本文最后建立了基于RBF神經網絡的診斷模型。結果表明在判別閾值為0.85時,單獨運用RBF神經網絡故障診斷方法可以在診斷精度稍低的情況下快速地判斷出故障原因。
[Abstract]:Petrochemical production is the basic industry of social development and the pillar industry of economic development. It plays a very important role in the process of modern social development.With the production structure and scale of petrochemical process being modernized, large-scale and complicated, the probability of production accident is increasing gradually.Therefore, it is imperative to make effective fault diagnosis to prevent or avoid accidents in petrochemical production process.In order to carry out effective fault diagnosis, a hybrid fault diagnosis method is proposed in this paper, which includes two aspects: fault monitoring and fault diagnosis. The application of deisobutane unit in a gas fractionation unit of a petrochemical company is studied and verified.Firstly, a fault monitoring model based on Principal Component Analysis (PCA) is established.The method of principal component analysis (PCA) is used to monitor the four online working conditions, and the monitoring results are consistent with the pre-set conditions. The results show that the PCA method can not only greatly reduce the dimension of data and simplify the calculation.Moreover, it can effectively monitor on-line and find fault in time.Then a diagnosis model based on error feedback neural back propagation (BPN) network is established.The results show that using BP neural network to construct fault diagnosis system can only judge the fault category of the fault, but can not judge the specific fault situation.DS(Dempster-Shafer evidence the theory of evidence is used to fuse the diagnosis results of BP neural network. The results show that the DS evidence theory can be used to diagnose the causes of faults effectively on the premise of predicting the types of faults belonging to the working conditions.However, the BP-DS method still has some shortcomings, such as long diagnosis time and large amount of calculation.Although Radial Basis function neural network is not widely used in BP network, its classification ability, approximation ability and training speed are all better than BP neural network. Therefore, a diagnosis model based on RBF neural network is established in this paper.The results show that when the threshold value is 0. 85, the fault causes can be quickly determined by using the RBF neural network fault diagnosis method alone under the condition of lower diagnostic accuracy.
【學位授予單位】：燕山大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TE96;TE65

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本文編號：1725842