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.
