22 research outputs found

    Avionic air data sensors fault detection and isolation by means of singular perturbation and geometric approach

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    Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system.Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system

    Real-time fault diagnosis and fault-tolerant control

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    Model based fault detection and isolation approach for actuator and sensor faults in a UAV

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    Thesis (MEng)--Stellenbosch University, 2021.ENGLISH ABSTRACT: This thesis presents the design and validation of model-based fault detection and isolation (FDI) approach for unmanned aerial vehicles (UAV). In safety-critical sys- tems such as chemical, nuclear plants and passenger aircraft, FDI is typically founded on hardware redundancy. In hardware redundancy, multiple actuators are spatially distributed to localise faults quickly, and sensor measurements are compared for consistency. The primary drawback with hardware redundancy is the increased installation complexity, weight, and costs. With modern computing technologies, model-based FDI offers a cost-effective, iterative and efficient FDI design process, verifiable with high fidelity computer-aided simulation (CAS). This thesis investigates the application of the Two-Stage Kalman filter (TSKF) to the problem of FDI. The TSKF solves the main deficiencies faced with the aug- mented state Kalman filter (ASKF), namely, numerical instability in ill-conditioned systems, and computational inefficiency where large parameter vectors are aug- mented. The TSKF approach utilises two parallel reduced-order KFs to estimate the system state and the parameter vectors separately. The UAV’s two rudders are not "isolable" because they produce identical moments. A novel active FDI (AFDI) method is proposed to isolate rudder actuator faults. The FDI displays high noise sensitivity under the evere Dryden turbulence model, resulting in high false detection and missed detection rates. A novel adap- tive technique is proposed to improve the robustness and sensitivity of the FDI. Unlike most methods which rely on a single scaling factor, the proposed adaptation technique employs multiple factors to weight the spread of fault parameter covari- ance matrix in the direction of flow of information, resulting in selective adaptation. Fault parameter variations are nonuniform in time and space. A static alarm threshold will induce high false alarms or missed alarms when set to low or too high, respectively. A novel adaptive threshold based on the normalised innovation squared (NIS) is proposed. A Monte Carlo campaign is carried out to validate the FDI while fault-sizes, the aircraft’s physical parameters, and disturbances are scat- tered, each with a distinct mean dispersion. The proposed strategy exhibits high robustness to noise and sensitivity to faults which indicates a reliable FDI.AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die ontwerp en validering van ‘n model-gebaseerde foutop- sporing en isolasie (“fault deteciton and isolation (FDI)”) tegniek vir onbemande lugvoertuie (“unmanned aerial vehicles (UAVs)”). In veiligheidskritieke stelsels soos chemiese aanlegte, kernkragaanlegte, en passasiersvliegtuie, word FDI gewoon- lik gebaseer op hardeware-oortolligheid. Vir hardeware-oortolligheid word verskeie aktueerders ruimtelik versprei om foute vinnig op te spoor, en sensormetings word vergelyk vir ooreenstemming. Die primêre nadeel van hardeware-oortolligheid is die verhoogde installasie-kompleksiteit, gewig en koste. Met moderne rekenaarteg- nologieë bied model-gebaseerde FDI ’n koste-effektiewe, iteratiewe en doeltref-fende FDI-ontwerpproses met ‘n hoë betroubaarheid wat bevestig kan word met rekenaargesteunde simulasie. Hierdie tesis ondersoek die toepassing van die twee-stadium Kalman filter (“two- stage Kalman filter (TSKF)”) op die probleem van FDI. Die TSKF los die belangrik- ste tekortkominge van die uitgebredie-toestand Kalman-filter (“augmented state Kalman filter (ASKF)”) op, naamlik numeriese onstabiliteit in swak gekondisioneerde stelsels, en berekeningsondoeltreffendheid waar groot parametervektore bygevoeg word. Die TSKF-benadering gebruik twee parallelle Kalman filters met vermin- derde orde om die stelseltoestand en die parametervektore afsonderlik af te skat. Die UAV se twee roere (“rudders”) is egter nie “isoleerbaar” nie omdat dit hulle identiese draaimoment veroorsaak. ’n Nuwe aktiewe FDI-metode (AFDI) word voorgestel om die roeraktueerderfoute te isoleer. Die FDI vertoon hoë sensitiwiteit vir geraas vanaf erge turbulensie soos gemod- elleer deur die Dryden-turbulensie-model, wat lei tot ‘n groot aantal vals deteksies en gemiste deteksies. ’n Nuwe aanpassingstegniek word voorgestel om die robu- ustheid en sensitiwiteit van die FDI te verbeter. Anders as die meeste metodes wat op een enkele skaalfaktor staatmaak, gebruik die voorgestelde aanpassingstegniek verskeie faktore om die verspreiding van die foutparameterkovariansiematriks in die rigting van informasievloei te weeg, wat lei tot selektiewe aanpassing. Foutparametervariasies is nie eenvormig in tyd of ruimte nie. ’n Statiese alar- mdrempel sal hoë vals deteksies of gemiste deteksies veroorsaak as dit onderskei-delik óf te laag óf te hoog gestel is. ’n Nuwe aanpassingsdrempel wat gebaseer is op die genormaliseerde innovasie kwadraat (NIS) word voorgestel. ’n Monte Carlo simulasieveldtog is uitgevoer om die FDI te toets met die foutgroottes, die fisiese parameters van die vliegtuig, en die steurings lukraak gevarieer elk met ’n duide- like gemiddelde verspreiding. Die voorgestelde strategie vertoon ’n hoë robuus- theid vir geraas en sensitiwiteit vir foute, wat dui op ’n betroubare FDI

    Unknown input observer approaches to robust fault diagnosis

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    This thesis focuses on the development of the model-based fault detection and isolation /fault detection and diagnosis (FDI/FDD) techniques using the unknown input observer (UIO) methodology. Using the UI de-coupling philosophy to tackle the robustness issue, a set of novel fault estimation (FE)-oriented UIO approaches are developed based on the classical residual generation-oriented UIO approach considering the time derivative characteristics of various faults. The main developments proposed are:- Implement the residual-based UIO design on a high fidelity commercial aircraft benchmark model to detect and isolate the elevator sensor runaway fault. The FDI design performance is validated using a functional engineering simulation (FES) system environment provided through the activity of an EU FP7 project Advanced Fault Diagnosis for Safer Flight Guidance and Control (ADDSAFE).- Propose a linear time-invariant (LTI) model-based robust fast adaptive fault estimator (RFAFE) with UI de-coupling to estimate the aircraft elevator oscillatory faults considered as actuator faults.- Propose a UI-proportional integral observer (UI-PIO) to estimate actuator multiplicative faults based on an LTI model with UI de-coupling and with added H∞ optimisation to reduce the effects of the sensor noise. This is applied to an example on a hydraulic leakage fault (multiplicative fault) in a wind turbine pitch actuator system, assuming that thefirst derivative of the fault is zero. - Develop an UI–proportional multiple integral observer (UI-PMIO) to estimate the system states and faults simultaneously with the UI acting on the system states. The UI-PMIO leads to a relaxed condition of requiring that the first time derivative of the fault is zero instead of requiring that the finite time fault derivative is zero or bounded. - Propose a novel actuator fault and state estimation methodology, the UI–proportional multiple integral and derivative observer (UI-PMIDO), inspired by both of the RFAFE and UI-PMIO designs. This leads to an observer with the comprehensive feature of estimating faults with bounded finite time derivatives and ensuring fast FE tracking response.- Extend the UI-PMIDO theory based on LTI modelling to a linear parameter varying (LPV) model approach for FE design. A nonlinear two-link manipulator example is used to illustrate the power of this method

    Model-based fault diagnosis for aerospace systems: a survey

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    http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided

    Fault Diagnosis and Performance Recovery Based on the Dynamic Safety Margin

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    The complexity of modern industrial processes makes high dependability an essential demand for reducing production loss, avoiding equipment damage, and increasing human safety. A more dependable system is a system that has the ability to: 1) detect faults as fast as possible; 2) diagnose them accurately; 3) recover the system to the nominal performance as much as possible. Therefore, a robust Fault Detection and Isolation (FDI) and a Fault Tolerant Control (FTC) system design have attained increased attention during the last decades. This thesis focuses on the design of a robust model-based FDI system and a performance recovery controller based on a new performance index called Dynamic Safety Margin (DSM). The DSM index is used to measure the distance between a predefined safety boundary in the state space and the system state trajectory as it evolves. The DSM concept, its computation methods, and its relationship to the state constraints are addressed. The DSM can be used in different control system applications; some of them are highlighted in this work. Controller design based on DSM is especially useful for safety-critical systems to maintain a predefined margin of safety during the transient and in the presence of large disturbances. As a result, the application of DSM to controller design and adaptation is discussed in particular for model predictive control (MPC) and PID controller. Moreover, an FDI scheme based on the analysis of the DSM is proposed. Since it is difficult to isolate different types of faults using a single model, a multi-model approach is employed in this FDI scheme. The proposed FDI scheme is not restricted to a special type of fault. In some faulty situations, recovering the system performance to the nominal one cannot be fulfilled. As a result, reducing the output performance is necessary in order to increase the system availability. A framework of FTC system is proposed that combines the proposed FDI and the controllers design based on DSM, in particular MPC, with accepted degraded performance in order to generate a reliable FTC system. The DSM concept and its applications are illustrated using simulation examples. Finally, these applications are implemented in real-time for an experimental two-tank system. The results demonstrate the fruitfulness of the introduced approaches

    Diagnosis and fault-tolerant control using set-based methods

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    The fault-tolerant capability is an important performance specification for most of technical systems. The examples showing its importance are some catastrophes in civil aviation. According to some official investigations, some air incidents are technically avoidable if the pilots can take right measures. But, relying on the skill and experience of the pilots, it cannot be guaranteed that reliable flight decisions are always made. Instead, if fault-tolerant strategies can be included in the decision-making procedure, it will be very useful for safer flight. Fault-tolerant control is generally classified into passive and active fault-tolerant control. Passive fault-tolerant control relies on robustness of controller, which can only provide limited fault-tolerant ability, while active fault-tolerant control turns to a fault detection and isolation module to obtain fault information and then actively take actions to tolerate the effect of faults. Thus, active fault-tolerant control generally has stronger fault-tolerant ability. In this dissertation, one focuses on active fault-tolerant control, which for this case considers model predictive control and set-based fault detection and isolation. Model predictive control is a successful advanced control strategy in process industry and has been widely used for processes such as chemistry and water treatment, because of its ability to deal with multivariable constrained systems. However, the performance of model redictive control has deep dependence on system-model accuracy. Realistically, it is impossible to avoid the effect of modelling errors, disturbances, noises and faults, which always result in model mismatch. Comparatively, model mismatch induced by faults is possible to be effectively handled by suitable fault-tolerant strategies. The objective of this dissertation is to endow model predictive control with fault-tolerant ability to improve its effectiveness. In order to reach this objective, set-based fault detection and isolation methods are used in the proposed fault-tolerant schemes. The important advantage of set-based fault detection and isolation is that it can make robust fault detection and isolation decisions, which is key for taking right fault-tolerant measures. This dissertation includes four parts. The first part introduces this research, presents the state of the art and gives an introduction of used research tools. The second part proposes set-based fault detection and isolation for actuator or=and sensor faults, which are involved in interval observers, invariant sets and set-membership estimation. First, the relationship between interval observers and invariant sets is investigated. Then, actuator and sensor faults are separately coped with depending on their own features. The third part focuses on actuator or=and sensor fault-tolerant model predictive control, where the control strategy is robust model predictive control (tube-based and min-max approaches). The last part draws some conclusions, summarizes this research and gives clues for the further work.La capacidad de los sistemas para tolerar fallos es una importante especificación de desempeño para la mayoría de sistemas. Ejemplos que muestran su importancia son algunas catástrofes en aviación civil. De acuerdo a investigaciones oficiales, algunos incidentes aéreos son técnicamente evitables si los pilotos pudiesen tomar las medidas adecuadas. Aun así, basándose en las habilidades y experiencia de los pilotos, no se puede garantizar que decisiones de vuelo confiables serán siempre posible de tomar. En cambio, si estrategias de tolerancia a fallos se pudieran incluir en el proceso de toma de decisión, los vuelos serían mucho más seguros. El control tolerante a fallos es generalmente clasificado en control pasivo y activo. El control pasivo se basa en la robustez del controlador, el cual sólo provee una habilidad limitada de tolerancia a fallos, mientras que el control tolerante a fallos de tipo activo se convierte en un modulo de detección y aislamiento de fallos que permite obtener información de éstos, y luego, activamente, tomar acciones para tolerar el efecto de dichos fallos. Así pues, el control activo generalmente tiene habilidades más fuertes de tolerancia a fallos. Esta tesis se enfoca en control tolerante a fallos activo, para lo cual considera el control predictivo basado en modelos y la detección y aislamiento de fallos basados en conjuntos. El control predictivo basado en modelos es una estrategia de control exitosa en la industria de procesos y ha sido ampliamente utilizada para procesos químicos y tratamiento de aguas, debido a su habilidad de tratar con sistemas multivariables con restricciones. A pesar de esto, el desempeño del control predictivo basado en modelos tiene una profunda dependencia de la precisión del modelo del sistema. Siendo realistas, es imposible evitar el efecto de errores de modelado, perturbaciones, ruidos y fallos, que siempre llevan a diferencias entre el modelo y el sistema real. Comparativamente, el error de modelo inducido por los fallos es posible de ser manejado efectivamente por estrategias adecuadas de control tolerante a fallos. Con el fin de alcanzar este objetivo, métodos de detección y aislamiento de fallos basados en conjuntos son utilizados en los esquemas de tolerancia a fallos propuestos en esta tesis. La ventaja importante de estas técnicas de detección y aislamiento de fallos basadas en conjuntos es que puede tomar decisiones robustas de detección y aislamiento, lo cual es clave para tomar medidas acertadas de tolerancia a fallos. Esta tesis esta dividida en cuatro partes. La primera parte es introductoria, presenta el estado del arte y hace una introducción a las herramientas de investigación utilizadas. La segunda parte expone la detección y aislamiento de fallos en actuadores y/o sensores, basándose en teoría de conjuntos, a partir de observadores de intervalo, y conjuntos invariantes. La tercera parte se enfoca en el control predictivo robusto (con enfoques basados tanto en tubos robustos como en min-max) con tolerancia a fallos en actuadores y/o sensores. La cuarta parte presenta algunas conclusiones, hace un resumen de esta investigación y da algunas ideas para trabajos futuros

    Design of a flight controller to achieve improved fault tolerance

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    In the last years, multirotor aerial vehicles have gained popularity both as consumer products and in professional applications. Safety is one of the main concerns during operation, and different approaches to fault tolerance have been proposed and continue to be developed. For a control system to be able to handle off-nominal situations, failures must be properly detected and identified; therefore, a fault detection and identification algorithm is required. Also, the control loop has to be accordingly modified to cope with each particular failure in the best way possible. These algorithms usually run on the vehicle’s low-level flight computer, imposing on it a large additional computational load. In this work, a fault detection and identification module is used to evaluate its impact in terms of additional processing time on a flight computer based on the Cortex-M3 microcontroller. While a highly optimized version of the algorithm is able to run, it still suggests potential hardware limitations for expanding the system capabilities. The evaluation of the same module on an improved flight computer design based on a Cortex-M7 micro-processor shows a significantly reduced footprint in the overall performance, allowing for the addition of an augmented method for faster failure detection

    Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system

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    Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety. This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations. Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors
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