本文選題：報(bào)警系統(tǒng)設(shè)計(jì) + 靜態(tài)收斂指標(biāo)　； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：工業(yè)過(guò)程中主要過(guò)程變量的變化可以反映被監(jiān)控設(shè)備的運(yùn)行狀況。報(bào)警器的作用是通過(guò)對(duì)過(guò)程變量采樣信號(hào)的處理,并將其與報(bào)警閾值比較,對(duì)設(shè)備異常狀態(tài)進(jìn)行監(jiān)測(cè)。在報(bào)警器設(shè)計(jì)中,學(xué)者普遍都把誤報(bào)率(FAR)、漏報(bào)率(MAR)和平均延遲時(shí)間(AAD)作為衡量報(bào)警器性能的指標(biāo)。在過(guò)程變量統(tǒng)計(jì)分布已知的假設(shè)下,傳統(tǒng)的報(bào)警器設(shè)計(jì)方法通常是基于前兩個(gè)指標(biāo)來(lái)優(yōu)化報(bào)警器的閾值等參數(shù)。由于設(shè)備實(shí)際運(yùn)行及狀態(tài)監(jiān)測(cè)中存在的各種不利因素影響,使得過(guò)程變量的統(tǒng)計(jì)分布難以準(zhǔn)確獲取。Dempster-Shafer(DS)證據(jù)理論在對(duì)不確定性信息的表示、推理和綜合處理方面相對(duì)于概率論具有其自身的優(yōu)勢(shì)。已有學(xué)者將信息融合思想引入報(bào)警器設(shè)計(jì)當(dāng)中,給出了基于報(bào)警證據(jù)更新/融合規(guī)則的報(bào)警器設(shè)計(jì)與優(yōu)化方法,取得了初步研究成果。本文對(duì)報(bào)警器設(shè)計(jì)中的報(bào)警證據(jù)生成、適用于報(bào)警證據(jù)的性能指標(biāo)制定以及報(bào)警證據(jù)參數(shù)優(yōu)化問(wèn)題展開(kāi)更為深入的研究,以增進(jìn)證據(jù)理論在工業(yè)報(bào)警器設(shè)計(jì)中的深度應(yīng)用,主要工作如下:(1)基于Sigmoid函數(shù)的報(bào)警器證據(jù)生成方法。在利用傳統(tǒng)分段梯形模糊隸屬度函數(shù)實(shí)現(xiàn)過(guò)程變量到相應(yīng)報(bào)警證據(jù)的變換時(shí),由于使用了分段函數(shù),難免造成過(guò)程變量所含信息的損失。針對(duì)此問(wèn)題,提出基于連續(xù)型Sigmoid(S)函數(shù)的報(bào)警證據(jù)生成方法,并通過(guò)理論證明和仿真數(shù)據(jù)統(tǒng)計(jì)實(shí)驗(yàn)說(shuō)明該種轉(zhuǎn)換是一種對(duì)過(guò)程變量所含信息的等價(jià)變換。(2)基于靜態(tài)收斂指標(biāo)的報(bào)警證據(jù)優(yōu)化方法。基于Jousselme證據(jù)距離,定義報(bào)警證據(jù)概率賦值靜態(tài)收斂指標(biāo)(SI),并進(jìn)一步分析證據(jù)生成時(shí)S函數(shù)中的參數(shù)與SI的對(duì)應(yīng)關(guān)系,以及報(bào)警器閾值、FAR/MAR與SI的對(duì)應(yīng)關(guān)系;以此為基礎(chǔ),引入對(duì)報(bào)警證據(jù)的精細(xì)化折扣,設(shè)計(jì)關(guān)于SI的目標(biāo)函數(shù),通過(guò)對(duì)當(dāng)前時(shí)刻所獲報(bào)警證據(jù)的折扣向量的優(yōu)化及S函數(shù)參數(shù)的調(diào)整提升報(bào)警證據(jù)的可靠性。(3)基于動(dòng)態(tài)收斂指標(biāo)的條件化報(bào)警證據(jù)線性組合更新方法。給出動(dòng)態(tài)收斂指標(biāo)(DI)的定義,在靜態(tài)收斂指標(biāo)優(yōu)化的基礎(chǔ)上,設(shè)計(jì)基于動(dòng)態(tài)收斂指標(biāo)的報(bào)警證據(jù)更新及參數(shù)優(yōu)化方法。通過(guò)與傳統(tǒng)報(bào)警器設(shè)計(jì)方法和線性組合證據(jù)更新方法的對(duì)比實(shí)驗(yàn)分析,說(shuō)明本文所提方法的優(yōu)越性。
[Abstract]:The variation of the main process variables in the industrial process can reflect the operation status of the monitored equipment. The function of the alarm is to monitor the abnormal state of the equipment by processing the process variable sampling signal and comparing it with the alarm threshold. In the design of the alarm system, the false alarm rate, false alarm rate (false alarm rate) and average delay time (AAD) are generally regarded as indicators to measure the performance of the alarm. Under the assumption that the statistical distribution of process variables is known, the traditional alarm design method is usually based on the first two indicators to optimize the alarm threshold and other parameters. Because of the influence of various adverse factors in the actual operation of the equipment and the condition monitoring, it is difficult for the statistical distribution of the process variables to obtain the exact representation of the uncertain information in the evidence theory of .Dempster-Shafern DSs. Reasoning and comprehensive processing have their own advantages over probability theory. Some scholars have introduced the idea of information fusion into the design of alarm device, and presented the design and optimization method of alarm device based on alarm evidence update / fusion rules, and obtained preliminary research results. In this paper, the generation of alarm evidence in the design of alarm system is studied more deeply, which is suitable for establishing the performance index of alarm evidence and optimizing the parameters of alarm evidence, so as to enhance the deep application of evidence theory in the design of industrial alarm. The main work is as follows: 1) the method of alarm evidence generation based on Sigmoid function. When the traditional piecewise trapezoidal fuzzy membership function is used to realize the transformation of process variables to corresponding alarm evidence, the information contained in process variables is inevitably lost because of the use of piecewise functions. In order to solve this problem, an alarm evidence generation method based on continuous Sigmoid function is proposed. It is proved by theory and simulation data statistics that this conversion is a kind of equivalent transformation of information contained in process variables. It is an alarm evidence optimization method based on static convergence index. Based on the evidence distance of Jousselme, the static convergence index of probability assignment of alarm evidence is defined, and the corresponding relation between the parameters of S function and SI, and the corresponding relation between alarm threshold and SI is analyzed. Introducing a refined discount on alarm evidence to design a target function for SI, By optimizing the discounted vector of the alarm evidence obtained at the present time and adjusting the parameters of the S-function, the reliability of the alarm evidence is improved. The linear combination updating method of conditional alarm evidence based on dynamic convergence index is proposed. On the basis of static convergence index optimization, an alarm evidence updating and parameter optimization method based on dynamic convergence index is designed. The advantages of the proposed method are illustrated by comparing with the traditional alarm design method and the linear combined evidence updating method.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP277

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本文編號(hào)：1955296