4 research outputs found
Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems
Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft
Towards Real-Time, On-Board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems
For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respond to uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from the NASA Swift UAS
Towards Real-time, On-board, Hardware-supported Sensor and Software Health Management for Unmanned Aerial Systems
For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respond to uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from the
NASA Swift UAS
An experimental study and evaluation of a new architecture for clinical decision support - integrating the openEHR specifications for the Electronic Health Record with Bayesian Networks
Healthcare informatics still lacks wide-scale adoption of intelligent decision
support methods, despite continuous increases in computing power and
methodological advances in scalable computation and machine learning, over
recent decades. The potential has long been recognised, as evidenced in the
literature of the domain, which is extensively reviewed.
The thesis identifies and explores key barriers to adoption of clinical decision
support, through computational experiments encompassing a number of technical
platforms. Building on previous research, it implements and tests a novel platform
architecture capable of processing and reasoning with clinical data. The key
components of this platform are the now widely implemented openEHR electronic
health record specifications and Bayesian Belief Networks.
Substantial software implementations are used to explore the integration of
these components, guided and supplemented by input from clinician experts and
using clinical data models derived in hospital settings at Moorfields Eye Hospital.
Data quality and quantity issues are highlighted. Insights thus gained are used to
design and build a novel graph-based representation and processing model for the
clinical data, based on the openEHR specifications. The approach can be
implemented using diverse modern database and platform technologies.
Computational experiments with the platform, using data from two clinical
domains – a preliminary study with published thyroid metabolism data and a
substantial study of cataract surgery – explore fundamental barriers that must be
overcome in intelligent healthcare systems developments for clinical settings. These
have often been neglected, or misunderstood as implementation procedures of
secondary importance. The results confirm that the methods developed have the
potential to overcome a number of these barriers.
The findings lead to proposals for improvements to the openEHR
specifications, in the context of machine learning applications, and in particular for
integrating them with Bayesian Networks. The thesis concludes with a roadmap for
future research, building on progress and findings to date