Approach based on particle filter and uncertainty graph to diagnosis for dynamic systems

In model-based diagnosis of dynamic systems, the scale of state space is exponential to both the number of components and time-steps. K-Best enumeration considers only K states with maximum probabilities at each time-step, which reduces the enumeration space. But state updating is not feasible when the system is complex or the diagnostic duration is long. An approach named PF_LUG was presented, which is based on particle filter and labeled uncertainty graph. The probability of state was approximated by the number of particles sampling the state. And the traditional enumeration was replaced by label matching in back-tracking process. It reduced the computation cost of time-step by moving the term from exponent to multiplier in complexity function. The experimental results show that the running time increases linearly via time-step and outperforms K-Best enumeration apparently.