基于自学习采样粒子滤波器的不完备故障空间交互诊断方法  

Interactive diagnosis in incomplete fault space based on self-learning sample particle filter

作　　者：余伶俐[1] 唐小林[1] 唐琎[1] 

机构地区：[1]中南大学 信息科学与工程学院,长沙410083

出　　处：《控制与决策》2012年第9期1331-1336,共6页Control and Decision

基　　金：中国博士后科学基金项目(20110491272,2010-2012);中南大学博士后科研经费项目(2010-2012);中央高校基本科研业务费专项资金项N(2012QNZT060)

摘　　要：针对不完备空间混合系统,提出一种基于自学习采样粒子滤波器(SLSPF)的交互诊断方法,融入自学习采样机制,利用自学习即时概率指导采样,以摆脱粒子滤波器对转移概率的依赖;结合自学习采样与诊断的动态交互方式调整模式空间,使粒子滤波器采样粒子数动态减少;同时给出了不完备信息空间的真实模式与未知模式阈值的决策条件,实验结果表明,尤其在高维状态空间下,SLSPF不仅可以保证粒子滤波器的诊断精度,而且能够提高计算效率。To solve the problem of hybrid system fault diagnosis in incomplete space, a dynamical fault diagnosis method based on self-learning sample particle filter（SLSPF） is presented. With the mechanism of self-learning sampling and real-time distribution probability directed sampling, SLSPF can break out of the dependence on transition probability. The combination of self-learning sampling and dynamic interactive diagnosis mode makes the sampling number of the filter tend to decrease dynamically and adjusts the mode space. The threshold value decision-making condition of real mode and unknown mode in the incomplete information space is given. Experiment results show that even if in the higher-dimensional space, SLSPF can still guarantee the particle filter diagnose precision and computational efficiency.

关 键 词：故障诊断 自学习采样 粒子滤波器 不完备空间 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]