序貫測(cè)試動(dòng)態(tài)優(yōu)化與多目標(biāo)優(yōu)化算法研究
發(fā)布時(shí)間：2018-11-07 12:30
【摘要】：由于電子系統(tǒng)結(jié)構(gòu)日趨復(fù)雜,對(duì)其性能及狀態(tài)的測(cè)試也變得愈加困難,因此,需要在系統(tǒng)設(shè)計(jì)時(shí)就將可測(cè)性作為一項(xiàng)指標(biāo)納入設(shè)計(jì)范疇。序貫測(cè)試問題的目的是生成一組總體測(cè)試代價(jià)最小的測(cè)試序列以識(shí)別系統(tǒng)的故障,對(duì)減少系統(tǒng)后期維護(hù)成本具有現(xiàn)實(shí)意義,本文對(duì)序貫測(cè)試問題進(jìn)行了深入研究,對(duì)不同方面的序貫測(cè)試問題提出了解決方法。目前進(jìn)行可測(cè)性輔助設(shè)計(jì)的軟件較少,且大多都是基于客戶端/服務(wù)器架構(gòu)(C/S架構(gòu)),本文使用基于瀏覽器/服務(wù)器(B/S架構(gòu))架構(gòu)對(duì)相關(guān)功能進(jìn)行設(shè)計(jì)與實(shí)現(xiàn)。本文主要研究工作如下:1.介紹了序貫測(cè)試問題及常用于解決序貫測(cè)試問題的AO*算法,由于AO*算法的性能主要取決于所選的啟發(fā)函數(shù),根據(jù)實(shí)際需要介紹了兩種不同的啟發(fā)函數(shù):可求取系統(tǒng)最優(yōu)測(cè)試代價(jià)的基于霍夫曼編碼的啟發(fā)函數(shù)和用于快速估算系統(tǒng)測(cè)試代價(jià)的基于信息熵的啟發(fā)函數(shù),并用兩個(gè)實(shí)例說明了這兩種啟發(fā)函數(shù)的區(qū)別及各自的使用范圍。2.由于序貫測(cè)試的相關(guān)參數(shù)在系統(tǒng)實(shí)際使用中經(jīng)常發(fā)生改變,本文提出根據(jù)具體發(fā)生的變化對(duì)已有的故障診斷樹進(jìn)行修改的算法,該算法利用了已有信息對(duì)原故障診斷樹進(jìn)行調(diào)整,相對(duì)于重新生成新的故障診斷樹的效率要高,且該算法需要判斷是否需要對(duì)原故障診斷樹進(jìn)行調(diào)整,適合在參數(shù)經(jīng)常發(fā)生波動(dòng)的情況下提高效率。3.首先說明了多目標(biāo)優(yōu)化問題,并將實(shí)際序貫測(cè)試問題遇到的多目標(biāo)問題與經(jīng)典多目標(biāo)問題相結(jié)合,并介紹一種基于多目標(biāo)極值的遺傳規(guī)劃優(yōu)化算法來解決這一問題,這種算法在遺傳操作的基礎(chǔ)上增加了分組、分配適應(yīng)度、擇優(yōu)等操作,在種群進(jìn)化的過程中選出非支配解,最終輸出一組非支配解供系統(tǒng)的設(shè)計(jì)人員參考使用。4.介紹了軟件的總體設(shè)計(jì),對(duì)軟件結(jié)構(gòu)有了系統(tǒng)的了解,并介紹了開發(fā)軟件使用的技術(shù)及數(shù)據(jù)結(jié)構(gòu)。然后對(duì)軟件的主要功能進(jìn)行了詳細(xì)介紹,系統(tǒng)建模模塊可用于系統(tǒng)自動(dòng)建�；蚴鞘謩�(dòng)輸入?yún)?shù),序貫測(cè)試模塊可以生成系統(tǒng)的故障診斷樹和相關(guān)的參數(shù)報(bào)告,動(dòng)態(tài)變化針對(duì)已生成的序貫診斷樹進(jìn)行修改,多目標(biāo)優(yōu)化可產(chǎn)生一組非支配解。
[Abstract]:Because the structure of electronic system is becoming more and more complex, it is more difficult to test the performance and state of electronic system. Therefore, it is necessary to include testability as an index in the design of electronic system. The purpose of the sequential test problem is to generate a set of test sequences with the lowest total test cost to identify the system faults, which is of practical significance to reduce the maintenance cost of the system in the later stage. In this paper, the sequential test problem is studied in depth. The solutions to the sequential test problems in different aspects are presented. At present, there are few software for testability aided design, and most of them are based on client / server architecture (C / S architecture). In this paper, browser / server (B / S) architecture is used to design and implement the related functions. The main work of this paper is as follows: 1. This paper introduces the sequential test problem and the AO* algorithm, which is often used to solve the sequential test problem. Because the performance of the AO* algorithm mainly depends on the selected heuristic function, According to the practical needs, two different heuristic functions are introduced: the Hoffman coding heuristic function which can be used to calculate the optimal test cost of the system and the information entropy based heuristic function used to estimate the system test cost quickly. Two examples are given to illustrate the difference between the two kinds of heuristic functions and their scope of use. 2. 2. Because the related parameters of sequential testing often change in the actual use of the system, this paper proposes an algorithm to modify the existing fault diagnosis tree according to the specific changes. The algorithm makes use of the existing information to adjust the original fault diagnosis tree, which is more efficient than the reconstruction of the new fault diagnosis tree, and the algorithm needs to determine whether the original fault diagnosis tree needs to be adjusted. Suitable in the case of frequent fluctuations in parameters to improve efficiency. 3. First, the multi-objective optimization problem is explained, and the multi-objective problem is combined with the classical multi-objective problem, and a genetic programming optimization algorithm based on multi-objective extremum is introduced to solve the problem. On the basis of genetic operation, this algorithm adds some operations, such as grouping, assigning fitness, selecting optimal operation, and selects the non-dominated solution in the process of population evolution. Finally, it outputs a group of non-dominated solutions for the reference of the designers of the system. 4. This paper introduces the overall design of the software, and gives a systematic understanding of the software structure, and introduces the technology and data structure used in the development of the software. Then the main functions of the software are introduced in detail. The system modeling module can be used to model the system automatically or input parameters manually, and the sequential test module can generate the fault diagnosis tree and related parameter reports of the system. The dynamic change is modified to the generated sequential diagnostic tree, and a set of non-dominated solutions can be generated by multi-objective optimization.
【學(xué)位授予單位】：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)】：TP18;TP311.52
本文編號(hào)：2316382
[Abstract]:Because the structure of electronic system is becoming more and more complex, it is more difficult to test the performance and state of electronic system. Therefore, it is necessary to include testability as an index in the design of electronic system. The purpose of the sequential test problem is to generate a set of test sequences with the lowest total test cost to identify the system faults, which is of practical significance to reduce the maintenance cost of the system in the later stage. In this paper, the sequential test problem is studied in depth. The solutions to the sequential test problems in different aspects are presented. At present, there are few software for testability aided design, and most of them are based on client / server architecture (C / S architecture). In this paper, browser / server (B / S) architecture is used to design and implement the related functions. The main work of this paper is as follows: 1. This paper introduces the sequential test problem and the AO* algorithm, which is often used to solve the sequential test problem. Because the performance of the AO* algorithm mainly depends on the selected heuristic function, According to the practical needs, two different heuristic functions are introduced: the Hoffman coding heuristic function which can be used to calculate the optimal test cost of the system and the information entropy based heuristic function used to estimate the system test cost quickly. Two examples are given to illustrate the difference between the two kinds of heuristic functions and their scope of use. 2. 2. Because the related parameters of sequential testing often change in the actual use of the system, this paper proposes an algorithm to modify the existing fault diagnosis tree according to the specific changes. The algorithm makes use of the existing information to adjust the original fault diagnosis tree, which is more efficient than the reconstruction of the new fault diagnosis tree, and the algorithm needs to determine whether the original fault diagnosis tree needs to be adjusted. Suitable in the case of frequent fluctuations in parameters to improve efficiency. 3. First, the multi-objective optimization problem is explained, and the multi-objective problem is combined with the classical multi-objective problem, and a genetic programming optimization algorithm based on multi-objective extremum is introduced to solve the problem. On the basis of genetic operation, this algorithm adds some operations, such as grouping, assigning fitness, selecting optimal operation, and selects the non-dominated solution in the process of population evolution. Finally, it outputs a group of non-dominated solutions for the reference of the designers of the system. 4. This paper introduces the overall design of the software, and gives a systematic understanding of the software structure, and introduces the technology and data structure used in the development of the software. Then the main functions of the software are introduced in detail. The system modeling module can be used to model the system automatically or input parameters manually, and the sequential test module can generate the fault diagnosis tree and related parameter reports of the system. The dynamic change is modified to the generated sequential diagnostic tree, and a set of non-dominated solutions can be generated by multi-objective optimization.
【學(xué)位授予單位】：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)】：TP18;TP311.52
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 王顯;田恒;;基于測(cè)試-維修的多故障診斷策略研究[J];煤礦機(jī)械;2017年01期
2 趙文俊;張強(qiáng);匡翠婷;宋家友;;多值測(cè)試診斷策略優(yōu)化設(shè)計(jì)[J];計(jì)算機(jī)測(cè)量與控制;2015年12期
3 張娜;姚瀾;包曉安;董萌;桂寧;;多目標(biāo)優(yōu)化的測(cè)試用例優(yōu)先級(jí)在線調(diào)整策略[J];軟件學(xué)報(bào);2015年10期
4 羌曉清;景博;鄧森;焦曉璇;;基于Rollout算法的測(cè)試不可靠條件下的診斷策略[J];計(jì)算機(jī)應(yīng)用研究;2016年05期
5 黃以鋒;景博;毋養(yǎng)民;;分層系統(tǒng)序貫診斷策略[J];系統(tǒng)工程與電子技術(shù);2015年02期
6 Jing Qiu;Xiaodong Tan;Guanjun Liu;Kehong L;;Test selection and optimization for PHM based on failure evolution mechanism model[J];Journal of Systems Engineering and Electronics;2013年05期
7 冉紅亮;張琦;朱春生;王菲;;一種基于多目標(biāo)優(yōu)化的測(cè)試性分配方法[J];中國機(jī)械工程;2011年15期
8 溫熙森;邱靜;劉冠軍;;裝備可測(cè)性設(shè)計(jì)與評(píng)估技術(shù)綜述[J];國防科技;2009年01期
9 楊鵬;邱靜;劉冠軍;;測(cè)試不可靠條件下的診斷策略優(yōu)化方法[J];儀器儀表學(xué)報(bào);2008年04期
10 田仲;測(cè)試性分配方法研究[J];北京航空航天大學(xué)學(xué)報(bào);1999年05期
相關(guān)碩士學(xué)位論文 前2條
1 嚴(yán)俊豪;系統(tǒng)級(jí)測(cè)試性設(shè)計(jì)優(yōu)化方法與實(shí)現(xiàn)[D];電子科技大學(xué);2014年
2 詹琰;加權(quán)內(nèi)建自測(cè)試（BIST）設(shè)計(jì)中的多目標(biāo)優(yōu)化技術(shù)研究[D];桂林電子科技大學(xué);2011年
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