發(fā)布時(shí)間：2018-06-22 19:39

  本文選題：多目標(biāo)優(yōu)化問(wèn)題 + 測(cè)試選擇問(wèn)題��； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：由于新一代飛機(jī)對(duì)先進(jìn)航空電子系統(tǒng)的需求越來(lái)越高,航空電子技術(shù)在近年得到了迅速發(fā)展。在航空電子系統(tǒng)功能日益完善的同時(shí),其可靠性也得到越來(lái)越多研發(fā)人員的重視。航電健康管理系統(tǒng)作為航空電子系統(tǒng)可靠性的保障,逐漸成為研究熱點(diǎn),尤其是作為航電健康管理系統(tǒng)基礎(chǔ)的測(cè)試選擇問(wèn)題。經(jīng)典的測(cè)試選擇方法是單目標(biāo)方法,而實(shí)際上測(cè)試選擇問(wèn)題是典型的多目標(biāo)問(wèn)題。本文將多目標(biāo)優(yōu)化方法應(yīng)用到航電健康管理設(shè)計(jì)的測(cè)試選擇問(wèn)題中,完成了以下研究工作:(1)完成了多目標(biāo)優(yōu)化方法研究。多目標(biāo)優(yōu)化是當(dāng)前優(yōu)化和決策領(lǐng)域的熱點(diǎn)方向,它適用于存在多個(gè)需要同時(shí)考慮的優(yōu)化目標(biāo)并且這些優(yōu)化目標(biāo)往往互相沖突的情況。解決這類問(wèn)題需要特殊的建模方式、優(yōu)化理論和求解算法。因此,本文在具體研究了多目標(biāo)優(yōu)化問(wèn)題的數(shù)學(xué)模型和Pareto最優(yōu)理論的基礎(chǔ)上,詳細(xì)分析了MOGA、NSGA2、SPEA2、PAES這四種在多目標(biāo)優(yōu)化領(lǐng)域具有代表性,評(píng)價(jià)較高的算法,同時(shí)研究了多目標(biāo)優(yōu)化算法的性能評(píng)價(jià)方法。(2)詳細(xì)研究了測(cè)試選擇問(wèn)題,分析了其中多個(gè)優(yōu)化目標(biāo)的物理意義和計(jì)算方式。介紹了基于故障字典技術(shù)的測(cè)試選擇方法,研究了從初始故障字典構(gòu)建,模糊組劃分到整數(shù)編碼轉(zhuǎn)換的整個(gè)建模流程,還研究了貪婪選擇法和智能優(yōu)化法兩類測(cè)試選擇方法并分析了其優(yōu)缺點(diǎn)。同時(shí)提出了一種基于測(cè)試選擇優(yōu)化的故障診斷技術(shù)。(3)提出了基于混沌多目標(biāo)粒子群優(yōu)化算法的測(cè)試選擇方法。在離散粒子群算法的基礎(chǔ)上進(jìn)行了多目標(biāo)優(yōu)化的改進(jìn),特別加入混沌變異機(jī)制提高算法的全局搜索能力。實(shí)驗(yàn)證明混沌機(jī)制能避免算法出現(xiàn)早熟收斂現(xiàn)象,隨后通過(guò)與其它算法的對(duì)比實(shí)驗(yàn),驗(yàn)證了本文所提出的算法的有效性和卓越性。(4)完成了測(cè)試選擇及故障診斷軟件的設(shè)計(jì)與驗(yàn)證。結(jié)合本文提出的針對(duì)測(cè)試選擇問(wèn)題的多目標(biāo)優(yōu)化算法以及故障診斷技術(shù),設(shè)計(jì)了一款由測(cè)試選擇模塊、實(shí)時(shí)監(jiān)測(cè)模塊和故障診斷模塊組成的軟件,并且通過(guò)該軟件,驗(yàn)證了測(cè)試選擇以及多目標(biāo)優(yōu)化方法應(yīng)用的有效性。本文是多目標(biāo)優(yōu)化方法在航電健康管理系統(tǒng)中應(yīng)用的嘗試,為后續(xù)工作提供基礎(chǔ)和新的研究方向。
[Abstract]:Avionics technology has been developed rapidly in recent years due to the increasing demand for advanced avionics systems for new generation aircraft. As the function of avionics system becomes more and more perfect, more and more researchers pay attention to its reliability. Avionics health management system, as the guarantee of avionics system reliability, has gradually become a research hotspot, especially the test selection as the basis of avionics health management system. The classical test selection method is a single objective method, but in fact the test selection problem is a typical multi-objective problem. In this paper, the multi-objective optimization method is applied to the test selection problem of avionics health management design. The following research work is accomplished: (1) the multi-objective optimization method is studied. Multi-objective optimization is a hot topic in the field of optimization and decision-making. It is suitable for the situation where there are many optimization objectives that need to be considered simultaneously and these optimization objectives often conflict with each other. Solving this kind of problems requires special modeling, optimization theory and solving algorithm. Therefore, on the basis of studying the mathematical model and Pareto optimal theory of multi-objective optimization problem in detail, this paper analyzes in detail four algorithms which are representative and highly evaluated in the field of multi-objective optimization. At the same time, the performance evaluation method of multi-objective optimization algorithm is studied. (2) the test selection problem is studied in detail, and the physical meaning and calculation method of multiple optimization objectives are analyzed. This paper introduces the test selection method based on fault dictionary technology, and studies the whole modeling process from initial fault dictionary construction, fuzzy group partition to integer coding conversion. Two kinds of test selection methods, greedy selection method and intelligent optimization method, are also studied and their advantages and disadvantages are analyzed. At the same time, a fault diagnosis technique based on test selection optimization is proposed. (3) A test selection method based on chaotic multi-objective particle swarm optimization algorithm is proposed. Based on the discrete Particle Swarm Optimization (DPSO), the multi-objective optimization is improved, especially the chaos mutation mechanism is added to improve the global search ability of the algorithm. The experimental results show that the chaotic mechanism can avoid premature convergence of the algorithm, and then compared with other algorithms, The validity and excellence of the proposed algorithm are verified. (4) the design and verification of test selection and fault diagnosis software are completed. Combined with the multi-objective optimization algorithm and fault diagnosis technology proposed in this paper, a software composed of test selection module, real-time monitoring module and fault diagnosis module is designed. The validity of test selection and application of multi-objective optimization method is verified. This paper is an attempt to apply multi-objective optimization method in avionics health management system, which provides the basis and new research direction for the follow-up work.
【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)】：V243;O221.6

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本文編號(hào)：2054011