Picture of smart phone in human hand

World leading smartphone and mobile technology research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers from the Department of Computer & Information Sciences involved in researching exciting new applications for mobile and smartphone technology. But the transformative application of mobile technologies is also the focus of research within disciplines as diverse as Electronic & Electrical Engineering, Marketing, Human Resource Management and Biomedical Enginering, among others.

Explore Strathclyde's Open Access research on smartphone technology now...

Fault classification and diagnostic system for UAV electrical networks based on hidden Markov models

Telford, Rory and Galloway, Stuart (2015) Fault classification and diagnostic system for UAV electrical networks based on hidden Markov models. IET Electrical Systems in Transportation, 5 (3). pp. 103-111. ISSN 2042-9738

[img] PDF (Telford-Galloway-EST2015-diagnostic-system-for-unmanned-aerial-vehicle)
Telford_Galloway_EST2015_diagnostic_system_for_unmanned_aerial_vehicle.pdf - Final Published Version
License: Creative Commons Attribution 3.0 logo

Download (705kB)

Abstract

In recent years there has been an increase in the number of unmanned aerial vehicle (UAV) applications intended for various missions in a variety of environments. The adoption of the more-electric aircraft (MEA) has led to a greater emphasis on electrical power systems (EPS) for safe flight through an increased number of critical loads being sourced with electrical power. Despite extensive literature detailing the development of systems to detect UAV failures and enhance overall system reliability, few have focussed directly on the increasingly complex and dynamic EPS. This paper outlines the development of a novel UAV EPS fault classification and diagnostic (FCD) system based on hidden Markov models (HMM) that will assist and improve EPS health management and control. The ability of the proposed FCD system to autonomously detect, classify and diagnose the severity of diverse EPS faults is validated with development of the system for NASA’s Advanced Diagnostic and Prognostic Testbed (ADAPT), a representative UAV EPS system. EPS data from the ADAPT network was used to develop the FCD system and results described within this paper show that a high classification and diagnostic accuracy can be achieved using the proposed system.