# On the Selection of Calculable Residual Generators for UAV Fault Diagnosis

## TL;DR

This paper presents a methodology for selecting calculable residual generators for UAV fault diagnosis, combining prior and posterior information to efficiently identify minimal cost, implementable residuals in structural analysis.

## Contribution

It introduces a novel approach that integrates a priori and a posteriori data to improve the selection process of residual generators for UAV fault detection.

## Key findings

- Reduced time to find implementable residual generators
- Effective application to UAV fault diagnosis
- Enhanced reliability of structural analysis methods

## Abstract

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.