Fault detection and diagnosis for three-tank system using robust residual generator

Fault detection and diagnosis (FDD) is a task to deduce from observed variable of the system if any component is faulty, to locate the faults and also to estimate the fault magnitude present in the system. The main goal when synthesizing robust residual generators, for diagnosis and supervision, is to attenuate influence from model uncertainty on the residuals while keeping fault detection performance. In this paper, a design procedure for robust residual generators is developed with two key elements. One is the use of a reference model that represents desired performance. The other is an optimization criterion, based on robust H∞ filtering, used to synthesize the residual generator. Keywords: Fault detection, robust residual generation, structured residual approach, H-filtering.