On the Selection of Calculable Residual Generators for UAV Fault Diagnosis
For engineers designing fault diagnosis systems for UAVs, this method reduces the time to find implementable residual generators, though it is an incremental improvement over existing structural analysis techniques.
The paper proposes a methodology to select implementable, usable residual generators of minimum cost for fault diagnosis, reducing the search time. Applied to a fixed-wing UAV model, it improves the efficiency of finding valid analytical redundancy relations.
Structural Analysis is an established method for Fault Detection and Identification (FDI) in large-scale systems, enabling the discovery of Analytical Redundancy Relations (ARRs) which serve as residual generators. However, most techniques used to enumerate ARRs do not specify the matching used to calculate each of those ARRs. This can result in non-implementable or unusable residual generators, in the presence of non-invertibilities in the equations involved or in lack of computational tools. In this paper, we propose a methodology which combines a priori and a posteriori information in order to reduce the time required to find implementable, usable residual generators of minimum cost. The method is applied to a fixed-wing Unmanned Aerial Vehicle (UAV) model.