本文選題：狀態(tài)維修 + 故障模式�。� 參考：《江蘇科技大學(xué)》2017年碩士論文

【摘要】：狀態(tài)維修(CBM)是一種執(zhí)行方式基于設(shè)備運(yùn)行狀態(tài)監(jiān)測,利用監(jiān)測的狀態(tài)參數(shù)和各種分析手段判斷設(shè)備狀態(tài),安排適當(dāng)維修時(shí)間和維修方式的維修。本文圍繞狀態(tài)維修的重要組成部分--決策及決策過程進(jìn)行深入的研究,研究了基于設(shè)備重要度和故障模式特征的維修方式選擇方法、設(shè)備缺陷早期識(shí)別模型和缺陷狀態(tài)預(yù)知模型,并在此基礎(chǔ)上建立維修決策優(yōu)化模型;通過對(duì)這些模型進(jìn)行計(jì)算機(jī)仿真和案例研究,驗(yàn)證了決策過程相關(guān)模型的可行性和有效性。首先,從設(shè)備重要度和故障模式特征兩個(gè)角度研究了設(shè)備維修方式的選擇方法。研究了設(shè)備的重要度排序方法,分析了層次分析法以及熵值法的特點(diǎn)和適用條件,提出了兩者相結(jié)合的組合權(quán)重分析方法,利用蒙特卡洛模擬確保了方法的客觀性;研究了設(shè)備故障模式特征的識(shí)別方法,提出了一種改進(jìn)的布谷鳥搜索與最小二乘支持向量機(jī)相融合的算法,對(duì)設(shè)備故障模式進(jìn)行準(zhǔn)確識(shí)別;最后以船舶動(dòng)力設(shè)備為實(shí)例,驗(yàn)證了基于重要度排序及故障模式特征相結(jié)合的設(shè)備維修方式確定方法。其次,分析了設(shè)備運(yùn)行階段狀態(tài)的特點(diǎn),研究了設(shè)備缺陷狀態(tài)早期識(shí)別方法。從設(shè)備狀態(tài)劣化的角度考慮,對(duì)隱半馬爾可夫模型(HSMM)及需要解決的評(píng)估、解碼和學(xué)習(xí)的三個(gè)問題所對(duì)應(yīng)的前向后向算法、Viterbi算法和最大似然估計(jì)法進(jìn)行了研究;在隨機(jī)濾波理論的基礎(chǔ)上,針對(duì)設(shè)備衰退因子的確定與選擇,提出了一種考慮衰退因子的HSMM模型早期缺陷發(fā)生時(shí)刻識(shí)別及剩余壽命計(jì)算方法,通過仿真驗(yàn)證了該方法的有效性和可行性。最后,分析了維修行為及維修間隔期的決策及優(yōu)化問題。研究了基于單位時(shí)間維修費(fèi)用最少的維修決策優(yōu)化目標(biāo),并給出了求解方法;針對(duì)目前維修方式優(yōu)化目標(biāo)單一的問題,給出了基于設(shè)備可靠性最高及單位時(shí)間內(nèi)維修費(fèi)用最少相融合的多目標(biāo)決策優(yōu)化,建立了相應(yīng)的設(shè)備維修決策與目標(biāo)優(yōu)化模型,通過仿真驗(yàn)證了該方法的有效性。
[Abstract]:Condition-based maintenance (CBM) is a kind of execution method based on equipment running condition monitoring. The condition parameters of monitoring and various analytical means are used to judge the state of equipment and arrange the proper maintenance time and maintenance mode. In this paper, the decision making and decision process, an important part of state maintenance, is deeply studied, and the method of selecting maintenance mode based on the feature of equipment importance and fault mode is studied. Based on these models, the maintenance decision optimization model is established, and the feasibility and validity of these models are verified by computer simulation and case study. Firstly, the selection method of equipment maintenance mode is studied from the aspects of equipment importance and fault mode characteristics. The importance ranking method of equipment is studied, the characteristics and applicable conditions of AHP and entropy method are analyzed, and a combined weight analysis method combining them is put forward. The objectivity of the method is ensured by Monte Carlo simulation. The recognition method of fault pattern feature of equipment is studied, and an improved algorithm of cuckoo bird search and least square support vector machine is proposed to accurately identify the fault pattern of equipment. Finally, the ship power equipment is taken as an example. The method of determining equipment maintenance mode based on importance ranking and fault mode feature is verified. Secondly, the characteristics of equipment running state are analyzed, and the early identification method of equipment defect state is studied. From the point of view of equipment state deterioration, the forward and backward Viterbi algorithm and the maximum likelihood estimation method for the evaluation, decoding and learning of the hidden semi-Markov model (HSMMM) and the three problems needed to be solved are studied. Based on the theory of random filtering, a method for identifying the occurrence time of early defects and calculating the residual life of HSMM model considering the decay factor is proposed. The effectiveness and feasibility of the method are verified by simulation. Finally, the decision and optimization of maintenance behavior and interval are analyzed. This paper studies the maintenance decision optimization goal based on the minimum maintenance cost per unit time, and gives the solution method, aiming at the problem of the single target of the current maintenance mode optimization. The multi-objective decision optimization based on the combination of the highest reliability of equipment and the least maintenance cost per unit time is presented. The corresponding model of equipment maintenance decision and target optimization is established. The effectiveness of the method is verified by simulation.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TB11

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李紅芳;;HSMM模型在船舶故障診斷中的研究[J];艦船科學(xué)技術(shù);2016年18期

2 劉勤明;李亞琴;呂文元;葉春明;;基于自適應(yīng)隱式半馬爾可夫模型的設(shè)備健康診斷與壽命預(yù)測方法[J];計(jì)算機(jī)集成制造系統(tǒng);2016年09期

3 任麗娜;王智明;雷春麗;;數(shù)控機(jī)床貝葉斯可靠性評(píng)估模型的綜合評(píng)價(jià)方法[J];上海交通大學(xué)學(xué)報(bào);2016年07期

4 方凱;劉凌云;羅敏;陳志楚;;基于矩估計(jì)和假設(shè)檢驗(yàn)的數(shù)控裝備可靠性分析[J];湖北汽車工業(yè)學(xué)院學(xué)報(bào);2016年01期

5 王浩偉;徐廷學(xué);劉勇;;基于隨機(jī)參數(shù)Gamma過程的剩余壽命預(yù)測方法[J];浙江大學(xué)學(xué)報(bào)(工學(xué)版);2015年04期

6 張仕新;昝翔;李浩;韓朝帥;;狀態(tài)維修理論及剩余壽命預(yù)測的研究現(xiàn)狀與展望[J];兵工自動(dòng)化;2014年09期

7 賀淼;阮奇;鄭曉桂;黃藝玲;張龍;;自適應(yīng)布谷鳥搜索算法[J];計(jì)算機(jī)與應(yīng)用化學(xué);2014年08期

8 呂文元;鄭睿;;基于時(shí)間延遲的維修類型優(yōu)化組合模型及案例分析[J];系統(tǒng)工程理論與實(shí)踐;2013年07期

9 鄭洪清;周永權(quán);;一種自適應(yīng)步長布谷鳥搜索算法[J];計(jì)算機(jī)工程與應(yīng)用;2013年10期

10 王寧;孫樹棟;蔡志強(qiáng);李淑敏;;基于HSMM的兩階段設(shè)備缺陷狀態(tài)識(shí)別方法[J];計(jì)算機(jī)應(yīng)用研究;2011年12期

相關(guān)博士學(xué)位論文 前7條

1 李向前;復(fù)雜裝備故障預(yù)測與健康管理關(guān)鍵技術(shù)研究[D];北京理工大學(xué);2014年

2 熊堯;面向重型數(shù)控機(jī)床的服役可靠性評(píng)估方法及增長技術(shù)研究[D];華中科技大學(xué);2013年

3 邵偉;蒙特卡洛方法及在一些統(tǒng)計(jì)模型中的應(yīng)用[D];山東大學(xué);2012年

4 彭穎;基于退化隱式半馬爾科夫模型的設(shè)備健康預(yù)測及系統(tǒng)性維護(hù)策略研究[D];上海交通大學(xué);2011年

5 呂蓬;旋轉(zhuǎn)機(jī)械故障模式識(shí)別方法研究[D];華北電力大學(xué)（北京）;2010年

6 王英;設(shè)備狀態(tài)維修系統(tǒng)結(jié)構(gòu)與決策模型研究[D];哈爾濱工業(yè)大學(xué);2007年

7 張秀斌;視情維修決策模型及應(yīng)用研究[D];中國人民解放軍國防科學(xué)技術(shù)大學(xué);2003年

相關(guān)碩士學(xué)位論文 前3條

1 李俊;基于Petri網(wǎng)的機(jī)械系統(tǒng)可靠性分析與故障診斷研究[D];武漢科技大學(xué);2015年

2 李傳濤;基于狀態(tài)預(yù)測的設(shè)備管理功能模塊設(shè)計(jì)與實(shí)現(xiàn)[D];鄭州大學(xué);2011年

3 蔣太立;基于RCM理論的維修決策研究[D];武漢理工大學(xué);2006年

，

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