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系统级故障诊断

系统级故障诊断的相关文献在1995年到2021年内共计89篇,主要集中在自动化技术、计算机技术、数学、水路运输 等领域,其中期刊论文71篇、会议论文13篇、专利文献3892731篇;相关期刊38种,包括电子学报、计算机工程、计算机工程与科学等; 相关会议6种,包括2007全国开放式分布与并行计算学术年会、第二届中国测试学术会议、第八届全国容错计算学术会议等;系统级故障诊断的相关文献由115位作者贡献,包括张大方、宣恒农、谢兵等。

系统级故障诊断—发文量

期刊论文>

论文:71 占比:0.00%

会议论文>

论文:13 占比:0.00%

专利文献>

论文:3892731 占比:100.00%

总计:3892815篇

系统级故障诊断—发文趋势图

系统级故障诊断

-研究学者

  • 张大方
  • 宣恒农
  • 谢兵
  • 刘兵
  • 张润驰
  • 杨小帆
  • 刘田田
  • 孙丽萍
  • 杭后俊
  • 苗春玲
  • 期刊论文
  • 会议论文
  • 专利文献

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    • 徐景硕; 安阳; 嵇邵康
    • 摘要: 为了提高组合导航系统对故障的检测能力,提高系统整体的容错性能,论文在设计联邦滤波器的基础上,在系统级层面上研究了故障诊断方法,并通过Matlab仿真进行了比较和分析.仿真结果表明,基于状态 χ2检验法和残差 χ2检验法两者均能有效地检测出系统的故障,但它们在实际应用中均存在一些缺点,可以在此基础上对两种方法改进,进一步提高系统的容错能力.
    • GUI Wei-xia; LAN Ting; LU Qian
    • 摘要: 为了更高效地解决系统级故障诊断问题,首次将烟花算法应用到故障诊断Malek模型中.充分利用烟花算法在局部搜索和全局搜索方面良好的自调节能力,引入最小爆炸半径检测机制,并采用改进的爆炸算子、高斯变异操作和新的映射策略,得到一个新的系统级故障诊断算法.通过仿真实验表明,该算法能以较短的CPU运行时间判断出故障集,从而证明算法具有良好的稳定性和快速性,并能高效解决Malek模型下的系统级故障诊断问题.%In order to solve the system-level fault diagnosis problem more efficiently,the fireworks algorithm is firstly applied to the fault diagnosis on Malek model. Make full use of the good ability of self-regulation for local search and global search of the fireworks algorithm. The minimum explosion radius detection mechanism is cited in the article. And improving explosive operator,optimizing Gaussian mutation operator,and new mapping strategy are used to build a new system-level fault diagnosis algorithm on Malek model. Simulation results show that the fireworks algorithm can work out the fault sets with a shorter CPU running time,which proves that the algorithm has good stability and rapidity,and it can effectively solve the system-level fault diagnosis problem under the Malek model.
    • 郭晨; 张丽; 冷明
    • 摘要: 为了理清系统级故障诊断中各个可诊断性之间的关联关系、优缺点以及适用性,在充分分析系统级故障诊断中可诊断性的国内外研究现状的基础上,针对各个可诊断性的继承性关联关系、诊断能力、故障的限制性条件进行了研究,进而指出了可诊断性未来的发展方向和各种诊断方法的工程应用潜力与前景.%In order to find out the relationship,the advantages and disadvantages,and the scope of each diagnosable system,this paper determined the relationship among diagnosises,diagnosability and the restrictive conditions.Then it put forward the future development directions and engineering application potential and prospect of diagnostic method in system level diagnosis.
    • 归伟夏; 陆倩
    • 摘要: 为了快速和有效地诊断出大规模多处理器系统中的故障结点,首次将烟花算法应用于系统级故障诊断中.充分利用烟花算法具有很好的全局搜索能力和局部搜索能力的自调节机制特点,结合PMC模型的故障诊断模式特点设计约束方程,提出新的适应度函数,并优化了变异算子以及选择策略,得到系统级故障诊断优化算法.仿真实验表明该算法具有很好的稳定性和收敛性,并证明了算法的有效性.
    • 宣恒农; 赵冬; 苗春玲; 张润驰; 刘田田
    • 摘要: In order to diagnose the fault units in the system, this paper firstly uses the Mussels Wandering Optimization algorithm to solve the system-level fault diagnosis problem, proposes an efficient fault diagnosis algorithm—the Mussels Wandering Optimization Fault Diagnosis(MWOFD). Combining with the characteristics of system-level fault diagnosis it proposes the Mussels Wandering encoding and initialization, and designs the new fitness function according to equation constraint conditions that the diagnostic model has to meet, at the same time it optimizes the existing binary mapping algo-rithm. Finally, the new algorithm is compared with AD-FAFD algorithm, FAFD algorithm and EAFD algorithm experi-mentally. Experimental results show that MWOFD algorithm improves the diagnostic accuracy and efficiency of diagnosis effectively.%为了诊断出系统中的故障单元,首次将贝壳漫步优化算法用于解决系统级故障诊断问题,提出一种高效快速的诊断算法——MWOFD诊断(Mussels Wandering Optimization Fault Diagnosis)算法.结合系统级故障诊断的特点,设计了个体化编码及初始化的方法,并根据诊断模型所满足的方程约束重新设计了适应度函数,同时对二进制映射算法进行优化.最后将新算法与AD-FAFD算法,FAFD算法和EAFD算法进行实验对比,结果表明:MWOFD算法有效地提高了诊断正确率和诊断效率.
    • 冯海林; 雷花; 梁伦
    • 摘要: The system-level fault diagnosis,an efficient method,is an essential subject for the expanding multiprocessor system.In order to maintain the proper functioning of the system via locating or evading the fault nodes,the probability matrix diagnosis algorithm is studied under the PMC model in t diagnosable system.Firstly,according to the analysis result of the simulation experiments on the general probablility matrix diagnostic,the higher fault alarm rate is presented.The absolute fault nodes aggregation based on the syndrome matrix is introduced to identify some fault nodes,the nodes grouping is used to replenish the non-fault sets,and the rigorous condition is impaired.Finally,the modified probability matrix diagnosis algorithm is proposed to improve the diagnostic efficiency.Simulation experiments show that it keeps the superiority of high detection accuracy,reduces fault alarm rate with the nodes increasing,and confirms the impressive diagnostic efficiency and extensive application.%系统级故障诊断是提高多处理器系统可靠性的必要手段.为了有效定位多处理系统中的故障单元,该文建立了一种基于PMC模型t可诊断条件下的概率性矩阵诊断算法.首先对一般概率性矩阵诊断算法进行仿真分析获悉其具有较高的误检率,在诊断过程中引进绝对故障基和节点集团思想,通过计算绝对故障基以寻找系统中的部分故障处理机,集团用于将不确定状态的节点单元分类以补充正常节点集合,改善了原诊断的限制条件.仿真实验验证:改进后的概率性矩阵诊断算法保持了很高的检测精度,并且随着节点数的增多极大地降低了误检率,提高了诊断效果,使得该算法具有广泛的适用性.
    • 宣恒农; 苗春玲; 赵冬
    • 摘要: 首次将蝙蝠算法用于解决系统级故障诊断问题,从而提出了一种高效的诊断算法——蝙蝠故障诊断算法.在初始化阶段,种群被分成大、小两类,并采用不同的处理方式;根据系统级故障模型的特点,设计出了具有方程约束的适应度函数;为了平衡全局搜索与局部搜索,在速度更新公式中增加一个变系数;为实现寻址的离散化,对蝙蝠速度进行了二进制映射.仿真实验结果表明,蝙蝠故障诊断算法在迭代次数、诊断正确率和最优解的适应度等方面明显优于现有的具有代表性群智能诊断算法——FAFD算法.
    • 周宁; 梁家荣
    • 摘要: 网络系统级故障诊断是一种重要的针对网络节点进行故障诊断的方法。通过对网络系统级故障诊断的PMC模型和MM模型的t可诊断性进行分析,在确定的网络拓扑结构中构造扩展星型结构,利用图论的方法对给定的PMC模型和MM模型下的症状进行分析和论证,判断扩展星型结构根节点的状态。最后基于扩展星型结构判断网络节点状态的证明结果,提出一种新的针对已确定系统诊断度、并能构造出扩展星型结构的多处理器网络系统的系统级诊断算法———扩展星型结构算法。通过理论证明和实验结果表明:这种算法能够简单、快速并且正确地识别出处理器网络系统的故障节点,其时间复杂度为O(N),N表示处理器网络系统的节点个数。%Abstarct:Level fault diagnosis is a kind of important fault diagnosis in network system. By analyzing the property of t-fault conditional diagnosis of PMC fault model and MM fault model, we structure an extended star structure in a defined network topology and use the graph theory to analyze and demonstrate the symptoms of a given PMC model and MM model, then identify the state of the root node of the extended star structure. In the end, we propose a new system level fault diagnosis called extended star structure algorithm for the multi processor network system with extended star structure and certain diagnosis. The theoretical demonstration and experimental results show that this algorithm can easily, fast and correctly identify all faulty nodes in the multiprocessor network system, whose time complexity of the algorithm is O(N), where N is the number of the all nodes of the network.
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