本文關(guān)鍵詞： 化工過程 動態(tài)不確定因果圖 故障診斷 故障狀態(tài)預測 概率推理　出處：《北京化工大學》2015年碩士論文　論文類型：學位論文

【摘要】：化工過程具有規(guī)模大、高復雜性、高風險等特點,為了避免經(jīng)濟及生命的損失,在故障發(fā)生時,有效及時地檢測出故障是十分必要的。動態(tài)不確定因果圖(Dynamic Uncertain Causality Graph, DUCG)是用有向圖表達不同變量之間因果關(guān)系并進行推理的理論,可通過圖形簡潔地表達過程的因果關(guān)系并根據(jù)構(gòu)建的圖形知識庫給出故障事件的概率推理。由于其能根據(jù)證據(jù)信息降低復雜度,并能處理不完備知識及不確定的證據(jù)信息,對于目前規(guī)模龐大的化工過程故障診斷有著自身的優(yōu)勢�；谏鲜霰尘�,本文所做的研究工作如下：①運用DUCG實現(xiàn)化工過程的故障診斷,并針對化工過程的震蕩信號,對原DUCG系統(tǒng)的數(shù)據(jù)發(fā)送模塊做出改進,使之適用范圍更全面。②采用了TE (Tennessee Eastman)過程作為實驗對象,建立了包含54個變量、114條因果關(guān)系的DUCG模型。該模型對TE過程中的故障都得到較高診斷排序概率,仿真結(jié)果達到了100%,說明了DUCG是一種行之有效的方法。③在故障診斷的基礎(chǔ)上對故障的異常變量及狀態(tài)進行預測,預測結(jié)果雖與實際結(jié)果不完全一致,但可預測出大部分實際異常的變量,預測止確率達到70%,說明了該功能具有一定的有效性。
[Abstract]:Chemical process has the characteristics of large scale, high complexity, high risk and so on. In order to avoid the loss of economy and life, when the fault occurs. It is necessary to detect faults effectively and in time. Dynamic Uncertain Causality Graph. DUCGG is a theory that uses digraph to express causality and reason between different variables. The causality of the process can be expressed succinctly by the graph and the probability inference of the fault event can be given according to the constructed graphic knowledge base, because it can reduce the complexity according to the evidence information. And can deal with incomplete knowledge and uncertain evidence information, for the current large-scale chemical process fault diagnosis has its own advantages. Based on the above background. The research work of this paper is as follows: 1. Using DUCG to realize the fault diagnosis of chemical process, and aiming at the oscillation signal of chemical process, the data sending module of the original DUCG system is improved. The te Tennessee Eastman process was used as the experimental object, and 54 variables were established. DUCG model of 114 causality. This model has a high probability of diagnosis and ranking for the faults in te process, and the simulation results reach 100%. It shows that DUCG is an effective method to predict the abnormal variables and states of faults on the basis of fault diagnosis. The prediction results are not completely consistent with the actual results. However, most of the actual abnormal variables can be predicted, and the accuracy rate of prediction reaches 70, which shows that this function is effective.
【學位授予單位】：北京化工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TQ02;TP277

【參考文獻】

相關(guān)期刊論文 前4條

1 ;[J];;年期

2 ;[J];;年期

3 ;[J];;年期

4 ;[J];;年期

，

本文編號：1490053