Reliability Modeling and Evaluation of Complex Multi-State System Based on Bayesian Networks Considering Fuzzy Dynamic of Faults

In the traditional reliability evaluation based on the Bayesian method,the failure probability of nodes is usually expressed by the average failure rate within a period of time.Aiming at the shortcomings of traditional Bayesian network reliability evaluation methods,this paper proposes a Bayesian network reliability evaluation method considering dynamics and fuzziness.The fuzzy theory and the dynamic of component failure probability are introduced to construct the dynamic fuzzy set function.Based on the solving characteristics of the dynamic fuzzy set and Bayesian network,the fuzzy dynamic probability and fuzzy dynamic importance degree of the fault state of leaf nodes are solved.Finally,through the dynamic fuzzy reliability analysis of CNC machine tool hydraulic system balance circuit,the application of this method in system reliability evaluation is verified,which provides support for fault diagnosis of CNC machine tools.

基于模糊线性自抗扰的柔性多状态开关控制研究

为抑制系统干扰对三端口柔性多状态开关(FMSS)直流侧电压的影响,提出了一种基于模糊线性自抗扰控制(LADRC)的三端口FMSS控制策略。首先,根据LADRC算法设计出LADRC控制器来替代电压外环和电流内环中的PI控制器,以此增强系统的抗干扰性能;其次,通过模糊控制改进电流内环的LADRC控制器,从而得到一种用于干扰抑制的模糊LADRC控制器,实现控制器参数可随扰动实时变化,以达到抑制干扰的目的;最后,在MATLAB/Simulink中建立三端口FMSS仿真模型,对控制策略进行仿真验证。仿真结果表明,与采用LADRC控制策略相比,所提控制策略具有更好的抗干扰性能和稳定性,能有效地抑制FMSS直流侧电压波动。

共因失效条件下多状态系统选择性维修优化

共因失效(CCF)广泛存在于多状态系统(MSS)中,研究共因失效条件下多状态系统的维修优化问题,对于提高系统任务成功概率、降低故障损失具有重要意义。考虑多状态系统、任务剖面和随机共因失效的模糊特性,采用模糊马尔可夫模型(FMM)和模糊通用生成函数(FUGF)法,建立了模糊随机共因失效条件下模糊多状态系统(FMSS)任务成功概率评估解析模型(AM),采用Monte Carlo仿真(MCS)方法对模型进行了验证;考虑维修费用约束,建立了模糊随机共因失效条件下模糊多状态系统选择性维修优化模型,为生成最优维修方案提供支持,结合实际案例,分析了随机共因失效对选择性维修优化和系统各状态的模糊概率的影响,验证了模型的有效性。研究结果表明,模糊随机共因失效和各模糊参数的α水平截集可能会影响选择性维修优化的结果,且忽略模糊随机共因失效会高估系统任务成功概率。