Evolution Strategies Based Particle Filters for Fault Detection

Recent massive increase of the computational power has allowed to rebirth of Monte Carlo integration and its application of Bayesian filtering, or particle filters. Particle filters evaluate a posterior probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, the filter performance is deteriorated by degeneracy phenomena in the importance weights. Recognizing the similarities and the difference of the processes between the particle filters and evolution strategies, an evolutionary computation approaches, a novel filter called the evolution strategies based particle filter (ESP) has been proposed to circumvent this difficulty and to improve the performance. Here, the ESP filter is applied to fault detection of nonlinear stochastic state space models. Its applicability is exemplified by numerical simulation studies