Cooperative Virtual Sensor for Fault Detection and Identification in Multi-UAV Applications

This paper considers the problem of fault detection and identification (FDI) in applications carried out by a group of unmanned aerial vehicles (UAVs) with visual cameras. In many cases, the UAVs have cameras mounted onboard for other applications, and these cameras can be used as bearing-only sensors to estimate the relative orientation of another UAV. The idea is to exploit the redundant information provided by these sensors onboard each of the UAVs to increase safety and reliability, detecting faults on UAV internal sensors that cannot be detected by the UAVs themselves. Fault detection is based on the generation of residuals which compare the expected position of a UAV, considered as target, with the measurements taken by one or more UAVs acting as observers that are tracking the target UAV with their cameras. Depending on the available number of observers and the way they are used, a set of strategies and policies for fault detection are defined. When the target UAV is being visually tracked by two or more observers, it is possible to obtain an estimation of its 3D position that could replace damaged sensors. Accuracy and reliability of this vision-based cooperative virtual sensor (CVS) have been evaluated experimentally in a multivehicle indoor testbed with quadrotors, injecting faults on data to validate the proposed fault detection methods.Comisión Europea H2020 644271Comisión Europea FP7 288082Ministerio de Economia, Industria y Competitividad DPI2015-71524-RMinisterio de Economia, Industria y Competitividad DPI2014-5983-C2-1-RMinisterio de Educación, Cultura y Deporte FP

Detectability subspaces and observer synthesis for two-dimensional systems

The notions of input-containing and detectability subspaces are developed within the context of observer synthesis for two-dimensional (2-D) Fornasini-Marchesini models. Specifically, the paper considers observers which asymptotically estimate the local state, in the sense that the error tends to zero as the reconstructed local state evolves away from possibly mismatched boundary values, modulo a detectability subspace. Ultimately, the synthesis of such observers in the absence of explicit input information is addressed

Aircraft Flight Envelope Determination using Upset Detection and Physical Modeling Methods

The development of flight control systems to enhance aircraft safety during periods of vehicle impairment or degraded operations has been the focus of extensive work in recent years. Conditions adversely affecting aircraft flight operations and safety may result from a number of causes, including environmental disturbances, degraded flight operations, and aerodynamic upsets. To enhance the effectiveness of adaptive and envelope limiting controls systems, it is desirable to examine methods for identifying the occurrence of anomalous conditions and for assessing the impact of these conditions on the aircraft operational limits. This paper describes initial work performed toward this end, examining the use of fault detection methods applied to the aircraft for aerodynamic performance degradation identification and model-based methods for envelope prediction. Results are presented in which a model-based fault detection filter is applied to the identification of aircraft control surface and stall departure failures/upsets. This application is supported by a distributed loading aerodynamics formulation for the flight dynamics system reference model. Extensions for estimating the flight envelope due to generalized aerodynamic performance degradation are also described

Design and Performance Evaluation of Residual Generators for the FDI of an Aircraft

In this work, several procedures for the fault detection and isolation (FDI) on general aviation aircraft sensors are presented. In order to provide a comprehensive wide–spectrum treatment, both linear and nonlinear, model–based and data–driven methodologies are considered. The main contributions of the paper are related to the development of both FDI polynomial method (PM) and FDI scheme based on the nonLinear geometric approach (NLGA). As to the PM, the obtained results highlight a good trade–off between solution complexity and resulting performances. Moreover, the proposed PM is especially useful when robust solutions are required for minimising the effects of modelling errors and noise, while maximising fault sensitivity. As to the NLGA, the proposed work is the first development and robust application of the NLGA to an aircraft model in flight conditions characterised by tight–coupled longitudinal and lateral dynamics. In order to verify the robustness of the residual generators related to the previous FDI techniques, the simulation results adopt a typical aircraft reference trajectory embedding several steady–state flight conditions, such as straight flight phases and coordinated turns. Moreover, the simulations are performed in the presence of both measurement and modelling errors. Finally, extensive simulations are used for assessing the overall capabilities of the developed FDI schemes and a comparison with neural networks (NN) and unknown input Kalman filter (UIKF) diagnosis methods is performed

Model based fault detection for two-dimensional systems

Fault detection and isolation (FDI) are essential in ensuring safe and reliable operations in industrial systems. Extensive research has been carried out on FDI for one dimensional (1-D) systems, where variables vary only with time. The existing FDI strategies are mainly focussed on 1-D systems and can generally be classified as model based and process history data based methods. In many industrial systems, the state variables change with space and time (e.g., sheet forming, fixed bed reactors, and furnaces). These systems are termed as distributed parameter systems (DPS) or two dimensional (2-D) systems. 2-D systems have been commonly represented by the Roesser Model and the F-M model. Fault detection and isolation for 2-D systems represent a great challenge in both theoretical development and applications and only limited research results are available. In this thesis, model based fault detection strategies for 2-D systems have been investigated based on the F-M and the Roesser models. A dead-beat observer based fault detection has been available for the F-M model. In this work, an observer based fault detection strategy is investigated for systems modelled by the Roesser model. Using the 2-D polynomial matrix technique, a dead-beat observer is developed and the state estimate from the observer is then input to a residual generator to monitor occurrence of faults. An enhanced realization technique is combined to achieve efficient fault detection with reduced computations. Simulation results indicate that the proposed method is effective in detecting faults for systems without disturbances as well as those affected by unknown disturbances.The dead-beat observer based fault detection has been shown to be effective for 2-D systems but strict conditions are required in order for an observer and a residual generator to exist. These strict conditions may not be satisfied for some systems. The effect of process noises are also not considered in the observer based fault detection approaches for 2-D systems. To overcome the disadvantages, 2-D Kalman filter based fault detection algorithms are proposed in the thesis. A recursive 2-D Kalman filter is applied to obtain state estimate minimizing the estimation error variances. Based on the state estimate from the Kalman filter, a residual is generated reflecting fault information. A model is formulated for the relation of the residual with faults over a moving evaluation window. Simulations are performed on two F-M models and results indicate that faults can be detected effectively and efficiently using the Kalman filter based fault detection. In the observer based and Kalman filter based fault detection approaches, the residual signals are used to determine whether a fault occurs. For systems with complicated fault information and/or noises, it is necessary to evaluate the residual signals using statistical techniques. Fault detection of 2-D systems is proposed with the residuals evaluated using dynamic principal component analysis (DPCA). Based on historical data, the reference residuals are first generated using either the observer or the Kalman filter based approach. Based on the residual time-lagged data matrices for the reference data, the principal components are calculated and the threshold value obtained. In online applications, the T2 value of the residual signals are compared with the threshold value to determine fault occurrence. Simulation results show that applying DPCA to evaluation of 2-D residuals is effective.Doctoral These

Autonomous and Resilient Management of All-Source Sensors for Navigation Assurance

All-source navigation has become increasingly relevant over the past decade with the development of viable alternative sensor technologies. However, as the number and type of sensors informing a system increases, so does the probability of corrupting the system with sensor modeling errors, signal interference, and undetected faults. Though the latter of these has been extensively researched, the majority of existing approaches have constrained faults to biases, and designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with valid measurement models, none of which are guaranteed for all-source systems. This research aims to provide all-source multi-sensor resiliency, assurance, and integrity through an autonomous sensor management framework. The proposed framework dynamically places each sensor in an all-source system into one of four modes: monitoring, validation, calibration, and remodeling. Each mode contains specific and novel realtime processes that affect how a navigation system responds to sensor measurements. The monitoring mode is driven by a novel sensor-agnostic fault detection, exclusion, and integrity monitoring method that minimizes the assumptions on the fault type, all-source sensor composition, and the number of faulty sensors. The validation mode provides a novel method for the online validation of sensors which have questionable sensor models, in a fault-agnostic and sensor-agnostic manner, and without compromising the ongoing navigation solution in the process. The remaining two modes, calibration and remodeling, generalize and integrate online calibration and model identification processes to provide autonomous and dynamic estimation of candidate model functions and their parameters, which when paired with the monitoring and validation processes, directly enable resilient, self-correcting, plug-and-play open architecture navigation systems