Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design...Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test （UUT） in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis （FMEMECA）, and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.展开更多
针对多UUT(Unit Under Test)并行测试任务调度与资源配置问题,提出了一种遗传蚁群融合算法.应用遗传蚁群融合算法能快速、准确地寻找到具有最大成本效率的多UUT并行测试资源配置和任务序列.建立了多UUT并行测试任务资源描述的数学模型,...针对多UUT(Unit Under Test)并行测试任务调度与资源配置问题,提出了一种遗传蚁群融合算法.应用遗传蚁群融合算法能快速、准确地寻找到具有最大成本效率的多UUT并行测试资源配置和任务序列.建立了多UUT并行测试任务资源描述的数学模型,分析了多UUT测控资源合并的条件,得出最短并行测试时间基础上的最少资源需求,给出了成本效率的定义,设计了一种满足多UUT并行测试任务调度的基因编码方法和路径选择方案.算法初期利用遗传算法的快速收敛性,为蚁群算法提供初始信息素分布,蚁群算法采用双向收敛的信息素反馈方式,避免了对参数的依赖,减少了局部收敛性,加快了收敛速度.实例表明,该算法能很好地解决多UUT任务资源最优调度与配置问题.展开更多
提出了基于健康状态-广义测试相关性(health state-general test,简称HSGT)的健康状态评估技术。首先,根据维修要求将故障严重程度划分成多个离散的健康状态,再按测试输出属性,将测试划分为一系列区间值的广义测试,进而结合系统功能与...提出了基于健康状态-广义测试相关性(health state-general test,简称HSGT)的健康状态评估技术。首先,根据维修要求将故障严重程度划分成多个离散的健康状态,再按测试输出属性,将测试划分为一系列区间值的广义测试,进而结合系统功能与结构等信息,建立系统健康状态-广义测试相关性矩阵;其次,利用贝叶斯理论,建立基于HSGT的健康状态评估推理模型;最后,使用蒙特卡洛方法生成包含8个被测单元(unit under test,简称UUT)的系统,对所提技术的有效性及可行性进行了仿真验证。结果表明,提出的健康状态评估技术能根据系统测试输出结果,及时、准确地推理出系统中各个UUT的健康状态,评估结果能在故障加剧导致的功能失效前有效触发视情维修(condition based maintenance,简称CBM)的维修决策机制。展开更多
基金the National Natural Science Foundation of China(No.51175502)
文摘Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test （UUT） in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis （FMEMECA）, and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.
文摘针对多UUT(Unit Under Test)并行测试任务调度与资源配置问题,提出了一种遗传蚁群融合算法.应用遗传蚁群融合算法能快速、准确地寻找到具有最大成本效率的多UUT并行测试资源配置和任务序列.建立了多UUT并行测试任务资源描述的数学模型,分析了多UUT测控资源合并的条件,得出最短并行测试时间基础上的最少资源需求,给出了成本效率的定义,设计了一种满足多UUT并行测试任务调度的基因编码方法和路径选择方案.算法初期利用遗传算法的快速收敛性,为蚁群算法提供初始信息素分布,蚁群算法采用双向收敛的信息素反馈方式,避免了对参数的依赖,减少了局部收敛性,加快了收敛速度.实例表明,该算法能很好地解决多UUT任务资源最优调度与配置问题.
文摘提出了基于健康状态-广义测试相关性(health state-general test,简称HSGT)的健康状态评估技术。首先,根据维修要求将故障严重程度划分成多个离散的健康状态,再按测试输出属性,将测试划分为一系列区间值的广义测试,进而结合系统功能与结构等信息,建立系统健康状态-广义测试相关性矩阵;其次,利用贝叶斯理论,建立基于HSGT的健康状态评估推理模型;最后,使用蒙特卡洛方法生成包含8个被测单元(unit under test,简称UUT)的系统,对所提技术的有效性及可行性进行了仿真验证。结果表明,提出的健康状态评估技术能根据系统测试输出结果,及时、准确地推理出系统中各个UUT的健康状态,评估结果能在故障加剧导致的功能失效前有效触发视情维修(condition based maintenance,简称CBM)的维修决策机制。
