利用粒子群优化进行多故障模糊检测
Exploiting Particle Swarm Optimization in Multiple Faults Fuzzy Detection
AI总结：
提出一种基于粒子群优化（PSO）的在线多故障模糊检测方法，通过优化隶属函数设计提高效率，并在三水箱液压系统仿真中与遗传算法（GA）对比验证有效性。
摘要：
本文首先提出了一种在线多故障检测方法。为了提高效率，需要对隶属函数进行优化设计。因此，采用粒子群优化（PSO）技术对所提出的方法进行了改进。所提出方法的输入是表示解析冗余关系数值评估的残差。这些残差是由于使用键合图建模而产生的。模糊检测模块的结果以彩色因果图的形式显示。最后，对使用PSO获得的结果与使用遗传算法（GA）获得的结果进行了比较。实验集中在诊断领域的基准——三水箱液压系统的仿真上。
英文摘要：
In this paper an on-line multiple faults detection approach is first of all proposed. For efficiency, an optimal design of membership functions is required. Thus, the proposed approach is improved using Particle Swarm Optimization (PSO) technique. The inputs of the proposed approaches are residuals representing the numerical evaluation of Analytical Redundancy Relations. These residuals are generated due to the use of bond graph modeling. The results of the fuzzy detection modules are displayed as a colored causal graph. A comparison between the results obtained by using PSO and those given by the use of Genetic Algorithms (GA) is finally made. The experiments focus on a simulation of the three-tank hydraulic system, a benchmark in the diagnosis domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
