In view of the problem that it is difficult to realize fault isolation when using the analytical redundancy relations-based fault diagnosis (ARRBFD) method

this paper presents a dual improved particle filter method for fault isolation. The method employs the joint estimation model composed of state and parameter estimation particle filters to jointly estimate the values of system state and potential fault parameter

and it achieves fault isolation by comparing the estimated value of the potential fault parameter with its nominal value. In the joint estimation model

on the basis of the traditional random perturbation method

an improved random perturbation method was developed to realize the parameter time update

which uses the maximum likelihood estimation method to obtain the parameter time update gradient. Then

a sampling method was proposed to improve the sampling particle quality

which takes into account the current measured values in the sampling process and introduces the idea of particle swarm optimization and simulated annealing optimization. Simulation results show that under the two types of parametric faults assumed in this paper

the joint estimation model based on dual particle filter outperformed the joint estimation model based on extended state space in terms of robustness

calculation speed

and estimation accuracy. The proposed method significantly improved the estimation performance in the joint estimation model based on the dual particle filter. In conclusion

the proposed method meets the requirements of computational efficiency and estimation accuracy for parametric fault isolation

and it can realize the fault isolation when applying the ARRBFD method.