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.