Fault classification and diagnostic system for unmanned aerial vehicle electrical networks based on hidden Markov models

Rory Telford,Stuart Galloway

doi:10.1049/iet-est.2014.0042

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 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 study 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 study show that a high classification and diagnostic accuracy can be achieved using the proposed system.

Highlights

The increasing trend of unmanned aerial vehicle (UAV) deployment for a variety of missions can mainly be attributed to the promise of reduced costs and reduced risk to human operators [1]
A UAV reliability investigation undertaken by the US Department of Defence [2] showed that the major sources of failure can mainly be divided into power/propulsion system flight control, communication and human/ground subsystems
Owing to the power system being integral to UAV reliability, the proper management of its health is imperative to UAV affordability, mission availability, operational efficiency and their acceptance into civil airspace

Summary

Introduction

The increasing trend of unmanned aerial vehicle (UAV) deployment for a variety of missions can mainly be attributed to the promise of reduced costs and reduced risk to human operators [1]. A UAV reliability investigation undertaken by the US Department of Defence [2] showed that the major sources of failure can mainly be divided into power/propulsion system flight control, communication and human/ground subsystems. UAV electrical power systems (EPS) operate in harsh environments and are characterised by physically compact topologies, where high-density generation provides energy to power electronics interfaced loads. Within the EPS, a diverse range of failure modes exist that have varying effect on network reliability; a major challenge is the design of fault tolerant control systems that can quickly detect and diagnose both critical and degraded faults to ensure robust health management and reliable operation. The network has a non-redundant power configuration of the EPS that supports mission and vehicle critical loads

Objectives

Findings

Discussion

Conclusion