A Hybrid Stochastic-Deterministic Approach For Active Fault Diagnosis Using Scenario Optimization

Active fault diagnosis can improve the diagnosability of potential faults by injecting a suitable input into the system. This input can be designed using either a stochastic or a deterministic framework. The stochastic approach aims to maximize the probability of a correct diagnosis at a certain time, whereas the deterministic approach aims to guarantee diagnosis within a certain time interval. Recently, a hybrid stochastic-deterministic approach has been developed in which all uncertainties are described by uniform probability density functions (PDFs) with finite support on zonotopes. This method is able to provide a guaranteed diagnosis at a given time N , while approximately maximizing the probability of diagnosis at some earlier time M < N . In this article, the hybrid stochastic-deterministic method is extended to arbitrary PDFs using a sampling approach based on scenario optimization.