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复杂系统失效率评估与多目标优化方法研究

作　者: Okafor Ekene Gabriel
导　师: 孙有朝
学　校: 南京航空航天大学
专　业: 载运工具运用工程
关键词: 复杂系统可靠性失效率多目标优化遗传算法
分类号: TP301.6
类　型: 博士论文
年　份: 2012年
下　载: 13次
引　用: 0次
阅　读: 论文下载

内容摘要

随着系统复杂程度的增加，系统失效率估计的难度也随之增大，这使得系统之间设计目标的权衡与协调的难度随之增加。通过多目标优化设计方法对系统进行设计是解决上述问题的有效方法之一。本文对复杂系统失效率估计方法、非约束多目标优化以及约束多目标优化设计方法开展了研究。在计算由并联或串联系统组成的复杂系统失效率时，基于其组成子系统或其运行时间的方法通常存在较大误差，有必要建立与复杂系统等效并能精确估计其失效率的方法。本文提出了一种将复杂系统减化成最小割集进而估计复杂系统失效率的方法，对由不同部件组成的串-并联系统和混合系统、相同部件组成的网络系统三种复杂系统失效率进行了估计，并与复杂系统简化方法估计的系统失效率进行对比。结果显示，本文提出的方法能够精确有效的估计复杂系统的失效率。系统复杂性的增加通常会导致多个目标相互冲突。通过对复杂系统多目标优化方法的研究，基于遗传算法和启发式搜索原则提出了GPSIA算法产生非支配最优解，并通过含有冗余的串-并联系统对GPSIA算法进行了验证，其中系统可靠性和费用作为优化目标，系统的重量作为约束，优化结果为均匀分布的非支配解，并用连续Pareto点的距离评价了算法的效果。通过与NSGA-II和SPEA-2算法的对比分析，对GPSIA算法的鲁棒性做了讨论。在实际问题的分析中，约束是广泛存在的。在基于GPSIA算法的优化策略中考虑了满足所有约束的可行解。非可行解会加快Pareto解的收敛速度。本部分在GPSIA方法的基础上，提出了GPSIA+DS算法，该算法在优化策略中同时考虑了可行解和非可行解。通过与GPSIA和NSGA-II算法在复杂系统优化中的对比分析，表明了GPSIA+DS算法的有效性。采用GSPIA+DS算法对某型飞机起落架位置指示和作动系统进行了多目标化，验证了该算法在可靠性工程中应用的有效性。

全文目录

摘要  4-5
ABSTRACT  5-16
CHAPTER 1 INTRODUCTION  16-21
  1.1 DISSERTATION OUTLINE  19-21
CHAPTER 2 LITERATURE REVIEW  21-42
  2.1 SYSTEM RELIABILITY PREDICTION  21-25
    2.1.1 COMPONENT RELIABILITY PREDICTION  21-22
    2.1.2 RELIABILITY PREDICTION FOR SERIES AND PARALLEL SYSTEMS  22-23
    2.1.3 RELIABILITY PREDICTION FOR SERIES-PARALLEL SYSTEMS  23-24
    2.1.4 RELIABILITY PREDICTION FOR COMPLEX SYSTEMS  24-25
  2.2 SYSTEM COST ESTIMATION  25-29
    2.2.1 TRADITIONAL COST ESTIMATION  26-27
    2.2.2 PARAMETRIC COST ESTIMATION  27-28
    2.2.3 FEATURE-BASED COST ESTIMATION  28
    2.2.4 QUALITATIVE COST ESTIMATION  28-29
  2.3 MULTI-OBJECTIVE OPTIMIZATION  29-40
    2.3.1 MULTI-OBJECTIVE OPTIMIZATION PROBLEM FORMULATION  29-30
    2.3.2 GENETIC ALGORITHM  30-31
    2.3.3 MULTI-OBJECTIVE GENETIC ALGORITHM  31-32
    2.3.4 DESIGN ISSUES AND COMPONENTS OF MULTI-OBJECTIVE GA  32-40
      2.3.4.1 Fitness Function  32-35
      2.3.4.2 Diversity  35-37
      2.3.4.3 Elitism  37-39
      2.3.4.4 Constraint Handling  39-40
      2.3.4.5 Hybrid Multi-Objective GA  40
  2.4 MULTI-OBJECTIVE GA FOR RELIABILITY OPTIMIZATION  40-42
CHAPTER 3 FAILURE RATE ESTIMATION FOR COMPLEX SYSTEM  42-56
  3.1 FAILURE RATE  43-45
    3.1.1 MEAN TIME TO FAILURE (MTTF)  44-45
  3.2 FAILURE RATE COMPUTATION FOR EXPONENTIALLY DISTRIBUTED SYSTEMS  45-49
    3.2.1 FAILURE RATE COMPUTATION FOR AN EXPONENTIALLY DISTRIBUTED SERIES SYSTEM  46-47
    3.2.2 FAILURE RATE COMPUTATION FOR AN EXPONENTIALLY DISTRIBUTED PARALLEL SYSTEM  47
    3.2.3 FAILURE RATE COMPUTATION FOR COMPLEX SYSTEMS  47-49
  3.3 THE PROPOSED METHOD  49-55
    3.3.1 OVERSIMPLIFIED METHOD  53-55
  3.4 CONCLUSION  55-56
CHAPTER 4 HYBRID MULTI-OBJECTIVE OPTIMIZATION ALGORITHM BASED ON GA AND HEURISTIC TECHNIQUE  56-80
  4.1 CLASSIFICATION OF MULTI-OBJECTIVE OPTIMIZATION  58-60
  4.2 GPSIA METHOD  60-69
    4.2.1 PROBLEM FORMULATION  60-63
    4.2.2 SOLUTION ENCODING  63
    4.2.3 INITIAL POPULATION  63-64
    4.2.4 CROSSOVER  64-65
    4.2.5 MUTATION  65
    4.2.6 PARETO SET SELECTION  65-67
    4.2.7 ALGORITHM  67-69
    4.2.8 TERMINATION CRITERIA  69
  4.3 PARETO SET SPACE  69-71
    4.3.1 PROPOSED SYSTEMATIC UNIFORMITY ASSESSMENT STRATEGY  70-71
  4.4 EXPERIMENTAL DESIGN  71-79
    4.4.1 GPSIA COMPARISON WITH NSGA-II AND SPEA-261  76-79
  4.5 CONCLUSION  79-80
CHAPTER 5 BIOLOGICALLY INSPIRED CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION ALGORITHM BASED ON GA  80-98
  5.1 CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION PROBLEM  81-82
  5.2 EXISTING CONSTRAINT HANDLING TECHNIQUES IN OPTIMIZATION  82-84
    5.2.1 PENALTY FUNCTION  82-83
    5.2.2 REPAIR ALGORITHM  83
    5.2.3 FEASIBILITY PRESERVING REPRESENTATION AND OPERATORS  83
    5.2.4 DECODER FUNCTIONS  83-84
    5.2.5 MULTI-OBJECTIVE OPTIMIZATION TECHNIQUES  84
  5.3 PROPOSED BIOLOGICALLY INSPIRED CONSTRAIN HANDLING TECHNIQUE  84-94
    5.3.1 TEST PROBLEM  86-89
    5.3.2 CONSTRAINED GPSIA: GPSIA+DS  89-94
      5.3.2.1 Solution Encoding  89-90
      5.3.2.2 Initial Population  90
      5.3.2.3 Crossover  90-91
      5.3.2.4 Mutation  91-92
      5.3.2.5 GPSIA+DS Algorithm  92-94
  5.4 COMPARATIVE STUDY  94-97
    5.4.1 PERFORMANCE METRICS  94-95
    5.4.2 EXPERIMENTAL DESIGN  95-96
    5.4.3 COMPARING GPSIA+DS WITH GPSIA  96
    5.4.4 COMPARING GPSIA+DS WITH NSGA-II+CD  96-97
  5.5 CONCLUSIONS  97-98
CHAPTER 6 MULTI-OBJECTIVE OPTIMIZATION OF AN AIRCRAFT POSITION INDICATOR AND ACTUATION SYSTEM  98-127
  6.1 PREVIOUS RELATED WORK  99-101
  6.2 LANDING GEAR SYSTEM  101-104
    6.2.1 MAIN LANDING GEAR (MLG)  101-102
    6.2.2 NOSE LANDING GEAR (NLG)  102
    6.2.3 AIRCRAFT POSITION INDICATION AND ACTUATION SYSTEM  102-104
  6.3 RESEARCH METHOD  104-124
    6.3.1 PIAS COMPONENT FAILURE RATE ESTIMATION  105-113
    6.3.2 PIAS COMPONENT WEIGHT ESTIMATION  113-121
    6.3.3 PIAS SOLUTION ENCODING  121-122
    6.3.4 PIAS INITIAL POPULATION  122
    6.3.5 PIAS CROSSOVER  122-123
    6.3.6 PIAS MUTATION  123-124
  6.4 EXPERIMENTAL DESIGN  124-126
  6.5 CONCLUSION  126-127
CHAPTER 7 CONCLUSIONS AND FUTURE WORK  127-130
  7.1 CONCLUSIONS  127-128
  7.2 FUTURE WORK  128-130
论文摘要  130-137
REFERENCES  137-147
ACKNOWLEDGEMENT  147-148
RESEARCH PUBLICATIONS  148-149
APPENDIX I  149-151
APPENDIX II  151

复杂系统失效率评估与多目标优化方法研究

内容摘要

全文目录

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