This research study is the result of a 3 years CIFRE PhD thesis between the Airbus design office(Aircraft Control domain) and TéSA laboratory in Toulouse. The main goal is to propose, developand validate a software solution for the detection and diagnosis of a specific type of elevator andrudder vibration, called limit cycle oscillation (LCO), based on existing signals available in flightcontrol computers on board in-series aircraft. LCO is a generic mathematical term defining aninitial condition-independent periodic mode occurring in nonconservative nonlinear systems. Thisstudy focuses on the LCO phenomenon induced by mechanical freeplays in the control surface ofa civil aircraft. The LCO consequences are local structural load augmentation, flight handlingqualities deterioration, actuator operational life reduction, cockpit and cabin comfort deteriorationand maintenance cost augmentation. The state-of-the-art for freeplay induced LCO detection anddiagnosis is based on the pilot sensitivity to vibration and to periodic freeplay check on the controlsurfaces. This study is thought to propose a data-driven solution to help LCO and freeplaydiagnosis. The goal is to improve even more aircraft availability and reduce the maintenance costsby providing to the airlines a condition monitoring signal for LCO and freeplays. For this reason,two algorithmic solutions for vibration and freeplay diagnosis are investigated in this PhD thesis. Areal time detector for LCO diagnosis is first proposed based on the theory of the generalized likeli hood ratio test (GLRT). Some variants and simplifications are also proposed to be compliantwith the industrial constraints. In a second part of this work, a mechanical freeplay detector isintroduced based on the theory of Wiener model identification. Parametric (maximum likelihoodestimator) and non parametric (kernel regression) approaches are investigated, as well as somevariants to well-known nonparametric methods. In particular, the problem of hysteresis cycleestimation (as the output nonlinearity of a Wiener model) is tackled. Moreover, the constrainedand unconstrained problems are studied. A theoretical, numerical (simulator) and experimental(flight data and laboratory) analysis is carried out to investigate the performance of the proposeddetectors and to identify limitations and industrial feasibility. The obtained numerical andexperimental results confirm that the proposed GLR test (and its variants/simplifications) is a very appealing method for LCO diagnostic in terms of performance, robustness and computationalcost. On the other hand, the proposed freeplay diagnostic algorithm is able to detect relativelylarge freeplay levels, but it does not provide consistent results for relatively small freeplay levels. Moreover, specific input types are needed to guarantee repetitive and consistent results. Further studies should be carried out in order to compare the GLRT results with a Bayesian approach and to investigate more deeply the possibilities and limitations of the proposed parametric method for Wiener model identification.

A caracterização de não linearidades em aeronaves é fundamental para a solução de problemas aeroelásticos, onde uma relação ótima entre desempenho e segurança é desejável. Sistemas aeroelásticos são inerentemente não lineares. Não linearidades podem ser admitidas no projeto, ou podem surgir a qualquer momento durante a vida útil da aeronave, afetando significativamente a resposta prevista por meios clássicos de análise linear, de forma que instabilidades catastróficas podem ocorrer antes dos limites de operação. Portanto, torna-se importante caracterizar, identificar e incluir tais efeitos não lineares no projeto e desenvolvimento de aeronaves. Neste trabalho, através da utilização de técnicas de análise de séries temporais não lineares e identificação, a não linearidade associada à folga, muito comum em superfícies de comando, é caracterizada de forma a permitir sua detecção em sinais aeroelásticos e inclusão de seus efeitos em modelos. Um modelo matemático de seção típica com folga na superfície de comando é implementado e utilizado para gerar uma base de dados confiável para testar a capacidade dos métodos de análise de séries temporais não lineares. As técnicas são aplicadas também em dados experimentais de uma asa a altos ângulos de ataque, sem um modelo matemático definido, para testar a capacidade de caracterização de comportamentos não lineares. Através de técnicas como reconstrução de espaço de estados, seções de Poincaré e determinação de invariantes, como os maiores expoentes de Lyapunov, os comportamentos e transições são classificados. Finalmente, as técnicas são aplicadas em dados experimentais de seções típicas com dois e três graus de liberdade, com folga no movimentos de torção e na superfície de comando, respectivamente. Os resultados mostram que uma folga pode gerar comportamentos similares aos apresentados por sistemas com não linearidade cúbica hardening em condições periódicas. No entanto, o comportamento subcrítico provocado pela folga, bem como a ocorrência de comportamento não linear mais complexo são características que diferenciam essas não linearidades. Em experimento com três graus de liberdade, a folga é localizada e caracterizada na superfície de comando através das técnicas propostas. Um modelo baseado em uma função aproximante para a folga é utilizado para identificar a resposta experimental e incluir a não linearidade no modelo matemático. Os resultados mostram que o modelo identificado é capaz de reproduzir a maior parte da dinâmica apresentada no experimento. / Characterization of nonlinearities in aircraft is critical to the solution of aeroelastic problems where an optimal relation between performance and safety is desirable. Aeroelastic systems are inherently nonlinear. Nonlinearities can be admitted in the project, or may arise at any time during the life of the aircraft, significantly affecting the predicted response by conventional methods of linear analysis and reducing the limits for catastrophic instabilities. Therefore, it is important to characterize, identify and include such non-linear effects to the design and development of aircraft. In this work, through nonlinear time series analysis and identification techniques, the freeplay nonlinearity, very common in control surfaces, is characterized to permit its detection in aeroelastic signals, and the inclusion of its effects in numerical models. A mathematical model of typical section with control surface freeplay nonlinearity is implemented and used to generate a reliable database to test the ability of nonlinear time series analysis methods. The techniques are also applied to experimental data of a wing at high angles of attack, without a prescribed mathematical model, to test the ability of characterizing non-linear behavior. Through techniques such as state space reconstruction, Poincaré sections and determination of system\'s invariants, as the largest Lyapunov exponents, non-linear behavior and transitions are classified. Finally, the techniques are applied to experimental data of typical sections with two and three degrees of freedom with freeplay in the torsional spring and in the control surface hinge, respectively. The results show that the freeplay may generate similar behavior to those presented by non-linear systems with cubic hardening nonlinearity under periodic conditions. However, the subcritical behavior caused by freeplay, and the occurrence of complex nonlinear behavior are features that distinguish these nonlinearities. In an experiment with three degrees of freedom, the freeplay is located in the control surface hinge and characterized by the presented techniques. A model based on an approximating function for the freeplay is used to identify the experimental response and include the nonlinearity in the mathematical model. It is shown that the identified model can reproduce the major part of the non-linear dynamics shown in the experiment.

Cette étude est le résultat d’une thèse CIFRE de trois ans entre le bureau d’étude d’Airbus (domaine du contrôle de l’avion) et le laboratoire TéSA à Toulouse. L’objectif principal est de proposer, développer et valider une solution logicielle pour la détection et le diagnostic d’un type spécifique de vibrations des gouvernes de profondeur et direction, appelée oscillation en cycle limite (limit cycle oscillation ou LCO en anglais), basée sur les signaux existants dans les avions civils. LCO est un terme mathématique générique définissant un mode périodique indépendant de conditions initiales et se produisant dans des systèmes non linéaires non conservatifs. Dans cette étude, nous nous intéressons au phénomène de LCO induit par les jeux mécaniques dans les gouvernes d’un avion civil. Les conséquences du LCO sont l’augmentation locale de la charge structurelle, la dégradation des qualités de vol, la réduction de la durée de vie de l’actionneur, la dégradation du confort du poste de pilotage et de la cabine, ainsi que l’augmentation des coûts de maintenance. L’état de l’art en matière de détection et de diagnostic du LCO induit par le jeu mécanique est basé sur la sensibilité du pilote aux vibrations et sur le contrôle périodique du jeu sur les gouvernes. Cette étude propose une solution basée sur les données (issues de la boucle d’asservissement des actionneurs qui agissent sur les gouvernes) pour aider au diagnostic du LCO et à l’isolement du jeu mécanique. L’objectif est d’améliorer encore plus la disponibilité des avions et de réduire les coûts de maintenance en fournissant aux compagnies aériennes un signal de contrôle pour le LCO et les jeux mécaniques. Pour cette raison, deux solutions algorithmiques pour le diagnostic des vibrations et des jeux ont été proposées. Un détecteur en temps réel pour la détection du LCO est tout d’abord proposé basé sur la théorie du rapport de vraisemblance généralisé (generalized likelihood ratio test ou GLRT en anglais). Certaines variantes et simplifications sont également proposées pour satisfaire les contraintes industrielles. Un détecteur de jeu mécanique est introduit basé sur l’identification d’un modèle de Wiener. Des approches paramétrique (estimateur de maximum de vraisemblance) et non paramétrique (régression par noyau) sont explorées, ainsi que certaines variantes des méthodes non paramétriques. En particulier, le problème de l’estimation d’un cycle d’hystérésis (choisi comme la non-linéarité de sortie d’un modèle de Wiener) est abordé. Ainsi, les problèmes avec et sans contraintes sont étudiés. Une analyse théorique, numérique (sur simulateur) et expérimentale (données de vol et laboratoire) est réalisée pour étudier les performances des détecteurs proposés et pour identifier les limitations et la faisabilité industrielle. Les résultats numériques et expérimentaux obtenus confirment que le GLRT proposé (et ses variantes / simplifications) est une méthode très efficace pour le diagnostic du LCO en termes de performance, robustesse et coût calculatoire. D’autre part, l’algorithme de diagnostic des jeux mécaniques est capable de détecter des niveaux de jeu relativement importants, mais il ne fournit pas de résultats cohérents pour des niveaux de jeu relativement faibles. En outre, des types d’entrée spécifiques sont nécessaires pour garantir des résultats répétitifs et cohérents. Des études complémentaires pourraient être menées afin de comparer les résultats de GLRT avec une approche Bayésienne et pour approfondir les possibilités et les limites de la méthode paramétrique proposée pour l’identification du modèle de Wiener. / This research study is the result of a 3 years CIFRE PhD thesis between the Airbus design office(Aircraft Control domain) and TéSA laboratory in Toulouse. The main goal is to propose, developand validate a software solution for the detection and diagnosis of a specific type of elevator andrudder vibration, called limit cycle oscillation (LCO), based on existing signals available in flightcontrol computers on board in-series aircraft. LCO is a generic mathematical term defining aninitial condition-independent periodic mode occurring in nonconservative nonlinear systems. Thisstudy focuses on the LCO phenomenon induced by mechanical freeplays in the control surface ofa civil aircraft. The LCO consequences are local structural load augmentation, flight handlingqualities deterioration, actuator operational life reduction, cockpit and cabin comfort deteriorationand maintenance cost augmentation. The state-of-the-art for freeplay induced LCO detection anddiagnosis is based on the pilot sensitivity to vibration and to periodic freeplay check on the controlsurfaces. This study is thought to propose a data-driven solution to help LCO and freeplaydiagnosis. The goal is to improve even more aircraft availability and reduce the maintenance costsby providing to the airlines a condition monitoring signal for LCO and freeplays. For this reason,two algorithmic solutions for vibration and freeplay diagnosis are investigated in this PhD thesis. Areal time detector for LCO diagnosis is first proposed based on the theory of the generalized likeli hood ratio test (GLRT). Some variants and simplifications are also proposed to be compliantwith the industrial constraints. In a second part of this work, a mechanical freeplay detector isintroduced based on the theory of Wiener model identification. Parametric (maximum likelihoodestimator) and non parametric (kernel regression) approaches are investigated, as well as somevariants to well-known nonparametric methods. In particular, the problem of hysteresis cycleestimation (as the output nonlinearity of a Wiener model) is tackled. Moreover, the constrainedand unconstrained problems are studied. A theoretical, numerical (simulator) and experimental(flight data and laboratory) analysis is carried out to investigate the performance of the proposeddetectors and to identify limitations and industrial feasibility. The obtained numerical andexperimental results confirm that the proposed GLR test (and its variants/simplifications) is a very appealing method for LCO diagnostic in terms of performance, robustness and computationalcost. On the other hand, the proposed freeplay diagnostic algorithm is able to detect relativelylarge freeplay levels, but it does not provide consistent results for relatively small freeplay levels. Moreover, specific input types are needed to guarantee repetitive and consistent results. Further studies should be carried out in order to compare the GLRT results with a Bayesian approach and to investigate more deeply the possibilities and limitations of the proposed parametric method for Wiener model identification.

A caracterização de não linearidades em aeronaves é fundamental para a solução de problemas aeroelásticos, onde uma relação ótima entre desempenho e segurança é desejável. Sistemas aeroelásticos são inerentemente não lineares. Não linearidades podem ser admitidas no projeto, ou podem surgir a qualquer momento durante a vida útil da aeronave, afetando significativamente a resposta prevista por meios clássicos de análise linear, de forma que instabilidades catastróficas podem ocorrer antes dos limites de operação. Portanto, torna-se importante caracterizar, identificar e incluir tais efeitos não lineares no projeto e desenvolvimento de aeronaves. Neste trabalho, através da utilização de técnicas de análise de séries temporais não lineares e identificação, a não linearidade associada à folga, muito comum em superfícies de comando, é caracterizada de forma a permitir sua detecção em sinais aeroelásticos e inclusão de seus efeitos em modelos. Um modelo matemático de seção típica com folga na superfície de comando é implementado e utilizado para gerar uma base de dados confiável para testar a capacidade dos métodos de análise de séries temporais não lineares. As técnicas são aplicadas também em dados experimentais de uma asa a altos ângulos de ataque, sem um modelo matemático definido, para testar a capacidade de caracterização de comportamentos não lineares. Através de técnicas como reconstrução de espaço de estados, seções de Poincaré e determinação de invariantes, como os maiores expoentes de Lyapunov, os comportamentos e transições são classificados. Finalmente, as técnicas são aplicadas em dados experimentais de seções típicas com dois e três graus de liberdade, com folga no movimentos de torção e na superfície de comando, respectivamente. Os resultados mostram que uma folga pode gerar comportamentos similares aos apresentados por sistemas com não linearidade cúbica hardening em condições periódicas. No entanto, o comportamento subcrítico provocado pela folga, bem como a ocorrência de comportamento não linear mais complexo são características que diferenciam essas não linearidades. Em experimento com três graus de liberdade, a folga é localizada e caracterizada na superfície de comando através das técnicas propostas. Um modelo baseado em uma função aproximante para a folga é utilizado para identificar a resposta experimental e incluir a não linearidade no modelo matemático. Os resultados mostram que o modelo identificado é capaz de reproduzir a maior parte da dinâmica apresentada no experimento. / Characterization of nonlinearities in aircraft is critical to the solution of aeroelastic problems where an optimal relation between performance and safety is desirable. Aeroelastic systems are inherently nonlinear. Nonlinearities can be admitted in the project, or may arise at any time during the life of the aircraft, significantly affecting the predicted response by conventional methods of linear analysis and reducing the limits for catastrophic instabilities. Therefore, it is important to characterize, identify and include such non-linear effects to the design and development of aircraft. In this work, through nonlinear time series analysis and identification techniques, the freeplay nonlinearity, very common in control surfaces, is characterized to permit its detection in aeroelastic signals, and the inclusion of its effects in numerical models. A mathematical model of typical section with control surface freeplay nonlinearity is implemented and used to generate a reliable database to test the ability of nonlinear time series analysis methods. The techniques are also applied to experimental data of a wing at high angles of attack, without a prescribed mathematical model, to test the ability of characterizing non-linear behavior. Through techniques such as state space reconstruction, Poincaré sections and determination of system\'s invariants, as the largest Lyapunov exponents, non-linear behavior and transitions are classified. Finally, the techniques are applied to experimental data of typical sections with two and three degrees of freedom with freeplay in the torsional spring and in the control surface hinge, respectively. The results show that the freeplay may generate similar behavior to those presented by non-linear systems with cubic hardening nonlinearity under periodic conditions. However, the subcritical behavior caused by freeplay, and the occurrence of complex nonlinear behavior are features that distinguish these nonlinearities. In an experiment with three degrees of freedom, the freeplay is located in the control surface hinge and characterized by the presented techniques. A model based on an approximating function for the freeplay is used to identify the experimental response and include the nonlinearity in the mathematical model. It is shown that the identified model can reproduce the major part of the non-linear dynamics shown in the experiment.