【摘要】：現(xiàn)代工業(yè)生產(chǎn)的大規(guī)模發(fā)展,為人類提供更豐富的物質(zhì)生活產(chǎn)品的同時,也帶來了災(zāi)難性事故危害的潛在風(fēng)險。為了提高工業(yè)生產(chǎn)的安全性,安全儀表系統(tǒng)(Safety Instrumented System, SIS)被越來越廣泛地應(yīng)用于現(xiàn)代流程工業(yè),以便在事故發(fā)生前能執(zhí)行其安全功能,避免事故的發(fā)生或減少事故發(fā)生后造成的危害。安全儀表系統(tǒng)作為流程工業(yè)安全生產(chǎn)的關(guān)鍵設(shè)施,對其自身的安全可靠性要求很高,但由于自身結(jié)構(gòu)、軟件、硬件、周圍環(huán)境及維護等原因,安全儀表系統(tǒng)本身也存在著安全性問題。為了確保安全生產(chǎn)、避免事故的發(fā)生,需要對安全儀表系統(tǒng)的可靠性進行準確的評估,因此,其可靠性評估方法的研究成為功能安全領(lǐng)域研究的熱點問題。 目前,對安全儀表系統(tǒng)可靠性評估的主要方法有五種,即：故障樹、可靠性框圖、簡化公式法、PDS及Markov模型。前四種模型計算簡單,但一次建模所考慮的性能指標有限,存在過多的條件假設(shè),無法動態(tài)的反應(yīng)安全儀表系統(tǒng)各狀態(tài)之間的動態(tài)變化。而Markov模型克服了前四種模型的不足,但也存在未考慮安全儀表系統(tǒng)的參數(shù)不確定性、模型的不完整性及人為因素等不確定性因素,使得評估結(jié)果仍有不足。由于人為因素較難量化,本文暫不考慮,而參數(shù)不確定是指Markov模型中假設(shè)各個狀態(tài)參數(shù)值為點值,但在實際應(yīng)用中各個狀態(tài)參數(shù)會隨著使用時間的推移而變化,是一個區(qū)間值。其次,為了提高安全儀表系統(tǒng)的可用性和可靠性系統(tǒng)通常采用冗余結(jié)構(gòu),而在多重冗余結(jié)構(gòu)中對安全可靠性影響最大的是共因失效因子,但Markov模型中采用的β因子模型,不同冗余結(jié)構(gòu)使用相同的共因失效因子β,模型存在不完整性。 為了解決Markov可靠性評估模型存在的不確定性問題,本文對現(xiàn)有的可靠性評估模型進行了分析研究。針對Markov模型應(yīng)用到SIS系統(tǒng)可靠性評估中存在的參數(shù)不確定性問題,本文將D-S證據(jù)理論的方法引入Markov模型,提出了DS-Markov模型,該模型通過引入D-S證據(jù)理論的信度函數(shù)和似真度函數(shù)來計算由各個狀態(tài)組成的辨識框架中失效率的區(qū)間值,得到兩個不同的Markov模型狀態(tài)轉(zhuǎn)移矩陣,根據(jù)不同的狀態(tài)轉(zhuǎn)移矩陣計算單元或系統(tǒng)的平均要求時失效概率,最終得到一個平均要求時失效概率的區(qū)間值。 其次,針對SIS可靠性評估中Markov模型的模型不完整性問題,本文結(jié)合β因子模型和多重β因子模型提出了一個新的共因失效因子模型—β*模型,β*模型不僅采用共因失效修正因子CMOON來區(qū)分不同的冗余結(jié)構(gòu)的共因失效因子,還考慮了安全儀表系統(tǒng)的自診斷性。 本文通過DS-Markov模型和Markov模型對常見的冗余結(jié)構(gòu)(1oo1、1oo2、2oo2、2oo3、1oo2D)單元進行可靠性評估,并對結(jié)果進行了分析：通過β*模型與β模型對2003冗余結(jié)構(gòu)單元評估并分析了評估結(jié)果；再把改進了的模型應(yīng)用到一個完整的安全儀表系統(tǒng)中驗證對其可靠性評估的準確度。實驗驗證表明本文提出的DS-Markov模型評估均值高于Markov模型評估得到的點值,DS-Markov模型評估準確度更高；本文提出β*模型評估結(jié)果均高于β模型,對安全儀表系統(tǒng)的可靠性評估更準確。
[Abstract]:The large-scale development of the modern industrial production also brings the potential risk of the disastrous accident hazard to the human being supplied with a richer material life product. In order to improve the safety of the industrial production, the safety instrument system (SIS) is widely used in the modern process industry so as to be able to carry out its safety function before the accident happens, to avoid the occurrence of the accident or to reduce the harm caused by the accident. As the key facility for safe production of the process industry, the safety instrument system is very high in its own safety and reliability, but the safety instrument system itself has security problems due to its own structure, software, hardware, surrounding environment and maintenance. In order to ensure the safety production and avoid the occurrence of accidents, the reliability of the safety instrument system needs to be accurately evaluated. Therefore, the reliability evaluation method is a hot issue in the field of functional safety. At present, there are five main methods to evaluate the system reliability of the safety instrument, namely, the fault tree, the reliability block diagram, the simplified formula method, the PDS and the Markov. The first four models are simple to calculate, but the performance index considered by one-time modeling is limited, there are too many condition hypotheses, and the dynamic reaction safety instrument system can not be used dynamically The Markov model overcomes the shortcomings of the first four models, but there are uncertainties such as the uncertainty of the parameters of the safety instrument system, the incompleteness of the model and the human factors. Not enough. This paper is not considered in this paper because the human factors are hard to quantify. The parameter uncertainty is the assumption that each state parameter value is a point value in the Markov model, but the state parameter in the actual application will change with the time of the use. It is a region. Secondly, in order to improve the availability and reliability system of the safety instrument system, the redundant structure is usually adopted, and the most important to the safety reliability in the multiple redundant structure is the failure factor, but the factor of the redundancy used in the Markov model The model and the different redundant structures use the same common cause failure factor, and the model is not finished. In order to solve the uncertainty of the Markov reliability evaluation model, this paper makes an analysis of the existing reliability evaluation model. In this paper, the method of D-S evidence theory is introduced into the Markov model and the DS-Mar is put forward. The model of the kov model, which is based on the reliability function and the degree of similarity function of the D-S evidence theory, is used to calculate the interval value of the failure efficiency in the identification frame, which is composed of various states, and two different Markov models are obtained. The state transition matrix is used to calculate the failure probability when the average requirement of the unit or system is calculated according to the different state transition matrix, and an average requirement is finally obtained. Secondly, based on the model of the Markov model in the reliability evaluation of the SIS, this paper presents a new common cause failure for the model of the Markov model in the reliability evaluation of the SIS. In this paper, the model of the sub-model is not only used for distinguishing the common failure factors of different redundant structures due to the failure correction factor CMOON, but also the safety instrument The self-diagnosis of the system is presented in this paper. In this paper, the reliability evaluation is carried out on the common redundant structures (1o1, 1o2, 2oo2, 2o3, and 1o2D) through the DS-Markov model and the Markov model, and the results are analyzed: the 2003 redundant structure is analyzed by the model of the model and the Markov model. The meta-evaluation and analysis of the evaluation results; and the application of the improved model to a complete safety instrument system for verification The results of the experiment show that the evaluation of the DS-Markov model is higher than the point value obtained by the Markov model evaluation, and the accuracy of the DS-Markov model is higher.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：X924.4;X913.4

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