Criticality of Prognostics in the Operations of Autonomous Aircraft

Abstract

This article addresses the design, testing, and evaluation of rigorous and verifiable prognostic and health management (PHM) functions applied to autonomous aircraft systems. These PHM functions—many deployed as algorithms—are integrated into a holistic framework for integrity management of aircraft components and systems that are subject to both operational degradation and incipient failure modes. The designer of a comprehensive and verifiable prognostics system is faced with significant challenges. Data (both baseline and faulted) that are correlated, time stamped, and appropriately sampled are not always readily available. Quantifying uncertainty, and its propagation and management, which are inherent in prognosis, can be difficult. High-fidelity modeling of critical components/systems can consume precious resources. Data mining tools for feature extraction and selection are not easy to develop and maintain. And finally, diagnostic and prognostic algorithms that address accurately the designer’s specifications are not easy to develop, verify, deploy, and sustain. These are just the technical challenges. On top of these are business challenges, for example, demonstrating that the PHM functionality will be economically beneficial to the system stakeholders, and finally, there are regulatory challenges, such as, assuring the authorities that the PHM system will have the necessary safety assurance levels while delivering its performance goals. This article tackles all three aspects of the use of PHM systems in autonomous systems. It outlines how some of the technical challenges have been overcome and demonstrates why PHM could be essential in this ecosystem and why regulatory authorities are increasingly open to the use of PHM systems even in the most safety-critical areas of aviation.