Diagnosis of airspeed measurement faults for unmanned aerial vehicles

Airspeed sensor faults are common causes for incidents with unmanned aerial vehicles with pitot tube clogging or icing being the most common causes. Timely diagnosis of such faults or other artifacts in signals from airspeed sensing systems could potentially prevent crashes. This paper employs parameter adaptive estimators to provide analytical redundancies and a dedicated diagnosis scheme is designed. Robustness is investigated on sets of flight data to estimate distributions of test statistics. The result is robust diagnosis with adequate balance between false alarm rate and fault detectability

A Framework for Diagnosis of Critical Faults in Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) need a large degree of tolerance towards faults. If not diagnosed and handled in time, many types of faults can have catastrophic consequences if they occur during flight. Prognosis of faults is also valuable and so is the ability to distinguish the severity of the different faults in terms of both consequences and the frequency with which they appear. In this paper flight data from a fleet of UAVs is analysed with respect to certain faults and their frequency of appearance. Data is taken from a group of UAV's of the same type but with small differences in weight and handling due to different types of payloads and engines used. Categories of critical faults, that could and have caused UAV crashes are analysed and requirements to diagnosis are formulated. Faults in air system sensors and in control surfaces are given special attention. In a stochastic framework, and based on a large number of data logged during flights, diagnostic methods are employed to diagnose faults and the performance of these fault detectors are evaluated against flight data. The paper demonstrates a significant potential for reducing the risk of unplanned loss of remotely piloted vehicles used by the Danish Navy for target practice.This is the authors' accepted and refereed manuscript to the article. Author's post-print must be released with a Creative Commons Attribution Non-Commercial No Derivatives License

Diagnosis of Wing Icing Through Lift and Drag Coefficient Change Detection for Small Unmanned Aircraft

This paper address the issue of structural change, caused by ice accretion, on UAVs by utilising a Neyman Pearson (NP) based statistical change detection approach, for the identi cation of structural changes of xed wing UAV airfoils. A structural analysis is performed on the nonlinear aircraft system and residuals are generated, where a generalised likelihood ratio test is applied to detect faults. Numerical simulations demonstrate a robust detection with adequate balance between false alarm rate and sensitivity.© 2015 Published by Elsevier Ltd. This is the authors' accepted and refereed manuscript to the article. Locked until 2017-01-01

Diagnosis of Icing and Actuator Faults in UAVs Using LPV Unknown Input Observers

This paper proposes a discrete-time linear parameter varying (LPV) unknown input observer (UIO) for the diagnosis of actuator faults and ice accretion in unmanned aerial vehicles (UAVs). The proposed approach, which is suited to an implementation on-board, exploits a complete 6-degrees of freedom (DOF) UAV model, which includes the coupled longitudinal/lateral dynamics and the impact of icing. The LPV formulation has the advantage of allowing the icing diagnosis scheme to be consistent with a wide range of operating conditions. The developed theory is supported by simulations illustrating the diagnosis of actuator faults and icing in a small UAV. The obtained results validate the effectiveness of the proposed approach

Control Surface Fault Diagnosis with Specified Detection Probability - Real Event Experiences

Diagnosis of actuator faults is crucial for aircraft since loss of actuation can have catastrophic consequences. For autonomous aircraft the steps necessary to achieve fault tolerance is limited when only basic and non-redundant sensor and actuators suites are present. Through diagnosis that exploits analytical redundancies it is, nevertheless, possible to cheaply enhance the level of safety. This paper presents a method for diagnosing control surface faults by using basic sensors and hardware available on an autonomous aircraft. The capability of fault diagnosis is demonstrated obtaining desired levels of false alarms and detection probabilities. Self-tuning residual generators are employed for diagnosis and are combined with statistical change detection to form a setup for robust fault diagnosis. On-line estimation of test statistics is used to obtain a detection threshold and a desired false alarm probability. A data based method is used to determine the validity of the methods proposed. Verification is achieved using real data and shows that the presented diagnosis method is efficient and could have avoided incidents where faults led to loss of aircraft.(c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Fault Detection and Fail-Safe Operation with a Multiple-Redundancy Air-Data System

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83640/1/AIAA-2010-7855-622.pd

Adaptive and Online Health Monitoring System for Autonomous Aircraft

Good situation awareness is one of the key attributes required to maintain safe flight, especially for an Unmanned Aerial System (UAS). Good situation awareness can be achieved by incorporating an Adaptive Health Monitoring System (AHMS) to the aircraft. The AHMS monitors the flight outcome or flight behaviours of the aircraft based on its external environmental conditions and the behaviour of its internal systems. The AHMS does this by associating a health value to the aircraft's behaviour based on the progression of its sensory values produced by the aircraft's modules, components and/or subsystems. The AHMS indicates erroneous flight behaviour when a deviation to this health information is produced. This will be useful for a UAS because the pilot is taken out of the control loop and is unaware of how the environment and/or faults are affecting the behaviour of the aircraft. The autonomous pilot can use this health information to help produce safer and securer flight behaviour or fault tolerance to the aircraft. This allows the aircraft to fly safely in whatever the environmental conditions. This health information can also be used to help increase the endurance of the aircraft. This paper describes how the AHMS performs its capabilities