Bayesian Diagnosability and Active Fault Identification

Abstract

We study fault identification in discrete-time nonlinear systems subject to additive Gaussian white noise. We introduce a Bayesian framework that explicitly accounts for unmodeled faults under reasonable assumptions. Our approach hinges on a new quantitative diagnosability definition, revealing when passive fault identification (FID) is fundamentally limited by the given control sequence. To overcome such limitations, we propose an active FID strategy that designs control inputs for better fault identification. Numerical studies on a two-water tank system and a Mars satellite with complex and discontinuous dynamics demonstrate that our method significantly reduces failure rates with shorter identification delays compared to purely passive techniques.