Uncertainty Quantification Framework for Autonomous System Tracking and Health Monitoring

Abstract

This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle.

Validation of a Physics-based Prognostic Model with Incomplete Data

“…To enable prognostics-based maintenance decision-making, it is important to include all sources of uncertainty in the RUL prediction (Sankararaman, 2015). These sources of uncertainty, which include measurement, modelling and usage uncertainty, have been reviewed by several researchers including Sankararaman (2015), Atamuradov, Medjaher, and Noureddine (2017) and Corbetta, Kulkarni, Banerjee, and Robinson (2021). Corbetta et al discusses four main categories of uncertainty sources, namely model, method, mea-sure and input.…”