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  本文選題：報警系統(tǒng)設(shè)計 + 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)過程中主要過程變量的變化可以反映被監(jiān)控設(shè)備的運行狀況。報警器的作用是通過對過程變量采樣信號的處理,并將其與報警閾值比較,對設(shè)備異常狀態(tài)進行監(jiān)測。在報警器設(shè)計中,學(xué)者普遍都把誤報率(FAR)、漏報率(MAR)和平均延遲時間(AAD)作為衡量報警器性能的指標(biāo)。在過程變量統(tǒng)計分布已知的假設(shè)下,傳統(tǒng)的報警器設(shè)計方法通常是基于前兩個指標(biāo)來優(yōu)化報警器的閾值等參數(shù)。由于設(shè)備實際運行及狀態(tài)監(jiān)測中存在的各種不利因素影響,使得過程變量的統(tǒng)計分布難以準(zhǔn)確獲取。Dempster-Shafer(DS)證據(jù)理論在對不確定性信息的表示、推理和綜合處理方面相對于概率論具有其自身的優(yōu)勢。已有學(xué)者將信息融合思想引入報警器設(shè)計當(dāng)中,給出了基于報警證據(jù)更新/融合規(guī)則的報警器設(shè)計與優(yōu)化方法,取得了初步研究成果。本文對報警器設(shè)計中的報警證據(jù)生成、適用于報警證據(jù)的性能指標(biāo)制定以及報警證據(jù)參數(shù)優(yōu)化問題展開更為深入的研究,以增進證據(jù)理論在工業(yè)報警器設(shè)計中的深度應(yīng)用,主要工作如下:(1)基于Sigmoid函數(shù)的報警器證據(jù)生成方法。在利用傳統(tǒng)分段梯形模糊隸屬度函數(shù)實現(xiàn)過程變量到相應(yīng)報警證據(jù)的變換時,由于使用了分段函數(shù),難免造成過程變量所含信息的損失。針對此問題,提出基于連續(xù)型Sigmoid(S)函數(shù)的報警證據(jù)生成方法,并通過理論證明和仿真數(shù)據(jù)統(tǒng)計實驗說明該種轉(zhuǎn)換是一種對過程變量所含信息的等價變換。(2)基于靜態(tài)收斂指標(biāo)的報警證據(jù)優(yōu)化方法。基于Jousselme證據(jù)距離,定義報警證據(jù)概率賦值靜態(tài)收斂指標(biāo)(SI),并進一步分析證據(jù)生成時S函數(shù)中的參數(shù)與SI的對應(yīng)關(guān)系,以及報警器閾值、FAR/MAR與SI的對應(yīng)關(guān)系;以此為基礎(chǔ),引入對報警證據(jù)的精細(xì)化折扣,設(shè)計關(guān)于SI的目標(biāo)函數(shù),通過對當(dāng)前時刻所獲報警證據(jù)的折扣向量的優(yōu)化及S函數(shù)參數(shù)的調(diào)整提升報警證據(jù)的可靠性。(3)基于動態(tài)收斂指標(biāo)的條件化報警證據(jù)線性組合更新方法。給出動態(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)計基于動態(tài)收斂指標(biāo)的報警證據(jù)更新及參數(shù)優(yōu)化方法。通過與傳統(tǒng)報警器設(shè)計方法和線性組合證據(jù)更新方法的對比實驗分析,說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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP277

【參考文獻】

相關(guān)期刊論文 前6條

1 徐曉濱;張鎮(zhèn);李世寶;文成林;;基于診斷證據(jù)靜態(tài)融合與動態(tài)更新的故障診斷方法[J];自動化學(xué)報;2016年01期

2 張世翔;章言鼎;;青島輸油管道泄漏爆炸事故分析與整改建議[J];工業(yè)安全與環(huán)保;2014年12期

3 戴晨鋮;;多傳感器信息融合綜述[J];科技視界;2012年26期

4 徐曉濱;文成林;劉榮利;;基于隨機集理論的多源信息統(tǒng)一表示與建模方法[J];電子學(xué)報;2008年06期

5 孫瑜;盧燕;;美國紐約大停電的原因及對策[J];青島理工大學(xué)學(xué)報;2007年06期

6 王國俊;模糊推理與模糊邏輯[J];系統(tǒng)工程學(xué)報;1998年02期

相關(guān)博士學(xué)位論文 前2條

1 徐曉濱;不確定性信息處理的隨機集方法及在系統(tǒng)可靠性評估與故障診斷中的應(yīng)用[D];上海海事大學(xué);2009年

2 李玉榕;信息融合與智能處理的研究[D];浙江大學(xué);2001年

相關(guān)碩士學(xué)位論文 前6條

1 劉平;基于區(qū)間值信度結(jié)構(gòu)的故障診斷方法研究[D];杭州電子科技大學(xué);2014年

2 陳樹峰;高頻電源電子線路故障診斷及輔助軟件設(shè)計[D];南京航空航天大學(xué);2013年

3 史健;基于證據(jù)理論的動態(tài)融合方法研究[D];杭州電子科技大學(xué);2013年

4 王迎昌;條件證據(jù)融合方法及其在故障診斷中的應(yīng)用[D];杭州電子科技大學(xué);2009年

5 王昭;基于工業(yè)數(shù)據(jù)的報警及預(yù)警系統(tǒng)研究[D];北京化工大學(xué);2008年

6 謝穎;信息融合算法研究及其應(yīng)用[D];重慶大學(xué);2008年

，

本文編號：1955296