Civil infrastructure throughout the world serves as main arteries for commerce and transportation, commonly forming the backbone of many societies. Bridges have been and remain a crucial part of the success of these civil networks. However, the crucial elements have been built over centuries and have been subject to generations of use. Many current bridges have outlived their intended service life or have been retrofitted to carry additional loads over their original design. A large number of these historic bridges are still in everyday use and their condition needs to be monitored for public safety. Transportation infrastructure authorities have implemented various inspection and management programs throughout the world, mainly visual inspections. However, careful visual inspections can provide valuable information but it has limitations in that it provides no actual stress-strain information to determine structural soundness. Structural Health Monitoring (SHM) has been a growing area of research as officials need to asses and triage the aging infrastructure with methods that provide measurable response information to determine the health of the structure. A rapid improvement in technology has allowed researchers to start using new sensors and algorithms to understand the structural parameters of tested structures due to known and unknown loading scenarios. One of the most promising methods involves the use of wireless sensor nodes to measure structural responses to loads in real time. The structural responses can be processed to help understand the modal parameters, determine the health of the structure, and potentially identify damage. For example, modal parameters of structures are typically used when designing the lateral system of a structure. A better understanding of these parameters can lead to better and more efficient designs. Usually engineers rely on a finite element analysis to identify these parameters. By observing the actual parameters displayed during field testing, the theoretical FE models can be validated for accuracy. This paper will present the field testing of a historic wrought iron truss bridge, in a case study, to establish a repeatable procedure to be used as reference for the testing of other similar structures.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24895号 / 工博第5175号 / 新制||工||1988(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 KIM Chul-Woo, 教授 杉浦 邦征, 教授 八木 知己 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

[pt] Ambientes industriais, especialmente no setor de petróleo e gás, apresentam desafios únicos para técnicas de identificação de sistemas. Apesar dos avanços, ainda existe uma lacuna em nossa compreensão da integração de algoritmos black box, abordagens grey box e aprendizado de máquina para simulação de dados reais. Com o objetivo de otimizar a compreensão e previsão em ambientes industriais complexos, foram explorados a simulação de dados do mundo real na produção de petróleo e análise da velocidade de perfuração. Este estudo propõe uma análise da integração de algoritmos black box, abordagens grey box e aprendizado de máquina na simulação de dados reais, com ênfase na produção de petróleo e o estudo da interação broca rocha no processo de perfuração de poços de petróleo. Neste trabalho foram empregados técnicas de aprendizado de máquina, como redes neurais e métodos clássicos de identificação de sistemas, como modelos lineares como ARX (AutoRegressive with eXogenous inputs) e não lineares como o NARX (Nonlinear AutoRegressive with eXogenous inputs), para capturar os comportamentos dinâmicos dos processos em estudo utilizando dados reais da produção de petróleo e de perfuração, levando em consideração as características específicas e desafios operacionais desses ambientes. Com base nos resultados obtidos, as técnicas utilizadas demonstraram viabilidade de aplicação, na qual a utilização desses modelos híbridos, que combinam conhecimentos físicos com abordagens de múltiplos modelos formados por algoritmos de identificação de sistemas e aprendizado de máquina, demonstrou potencial para aprimorar as simulações. Esses resultados ressaltam a eficácia desses métodos, indicando que pesquisas futuras podem se dedicar à implementação dessa técnica na identificação de sistemas complexos. / [en] Industrial environments, especially in the oil and gas sector, presentunique challenges for system identification techniques. Despite advancements,there still exists a gap in our understanding of integrating black box algorithms,grey box approaches, and machine learning for simulating real-world data.With the aim of optimizing understanding and prediction in complex industrialenvironments, real-world data simulation in oil production and drilling speedanalysis was explored. This study proposes an analysis of the integration ofblack box algorithms, grey box approaches, and machine learning in simulatingreal-world data, with an emphasis on oil production and the study of the drill-rock interaction in the oil well drilling process. In this work, machine learningtechniques such as neural networks and classical system identification methods,such as linear models like ARX (AutoRegressive with eXogenous inputs) andnonlinear ones like NARX (Nonlinear AutoRegressive with eXogenous inputs),were employed to capture the dynamic behaviors of the processes understudy. Additionally, real data from oil production and drilling were utilized,considering the specific characteristics and operational challenges of theseenvironments. Based on the results obtained, the techniques used demonstratedapplicability and yielded satisfactory outcomes. Specifically, the use of hybridmodels, combining physical knowledge with multiple model approaches formedby system identification algorithms and machine learning, showed potentialfor enhancing simulation. These findings underscore the effectiveness of thesemethods, suggesting that future research could focus on implementing thistechnique in identifying complex systems.

This study introduces new families of wavelets. The first is directly derived from the response of Second Order Underdamped Linear-Time-Invariant (SOULTI) systems, while the second is a generalization of the first to the complex domain and is similar to the Laplace transform kernel function. The first takes the acronym of SOULTI wavelet, while the second is named the Laplace wavelet. The most important criteria for a function or signal to be a wavelet is the ability to recover the original signal back from its continuous wavelet transform. It is shown that it is possible to recover back the original signal once the SOULTI or the Laplace wavelet transform is applied to decompose the signal. It is found that both wavelet transforms satisfy linear differential equations called the reconstructing differential equations, which are closely related to the differential equations that produce the wavelets. The new wavelets can have well defined Time-Frequency resolutions, and they have useful properties; a direct relation between the scale and the frequency, unique transform formulas that can be easily obtained for most elementary signals such as unit step, sinusoids, polynomials, and decaying harmonic signals, and linear relations between the wavelet transform of signals and the wavelet transform of their derivatives and integrals. The defined wavelets are applied to system analysis applications. The new wavelets showed accurate instantaneous frequency identification and modal decomposition of LTI Multi-Degree of Freedom (MDOF) systems and it showed better results than the Short-time Fourier Transform (STFT) and the other harmonic wavelets used in time-frequency analysis. The modal decomposition is applied for modal parameters identification, and the properties of the Laplace and the SOULTI wavelet transforms allows analytical and accurate identification methods. / Ph. D. / This study introduces new families of wavelets (small wave-like functions) derived from the response of Second Order Underdamped (oscillating) Linear-Time-Invariant systems. The first is named the SOULTI wavelets, while the second is named Laplace Wavelets. These functions can be used in a wavelet transform which transfers signals from the time domain to the time-frequency domain. It is shown that it is possible to recover back the original signal once the transform is applied. The new wavelets can have well defined Time-Frequency resolutions. The time-frequency resolution is the multiplication of the time resolution and the frequency resolution. A resolution is the smallest time range or frequency range that carries a feature of the signal. The new wavelets have useful properties; a direct relation between the scale and the frequency, unique transform formulas that can be easily obtained for most elementary signals such as unit step, sinusoids, polynomials, and decaying oscillating signals, and linear relations between the wavelet transform of signals and the wavelet transform of their derivatives and integrals. The defined wavelets are applied to system analysis applications. The new wavelets showed accurate instantaneous frequency identification, and decomposing signals into the basic oscillation frequencies, called the modes of vibration. In addition, the new wavelets are applied to infer the parameters of dynamic systems, and they show better results than the Short-time Fourier Transform (STFT) and the other wavelets used in time-frequency analysis.

Small unmanned aircraft and electric vertical takeoff and landing (eVTOL) aircraft have recently emerged as vehicles able to perform new missions and stimulate future air transportation methods. This dissertation presents several system identification research advancements for these modern aircraft configurations enabling accurate mathematical model development for flight dynamics simulations based on wind-tunnel and flight-test data. The first part of the dissertation focuses on advances in flight-test system identification methods using small, fixed-wing, remotely-piloted, electric, propeller-driven aircraft. A generalized approach for flight dynamics model development for small fixed-wing aircraft from flight data is described and is followed by presentation of novel flight-test system identification applications, including: aero-propulsive model development for propeller aircraft and nonlinear dynamic model identification without mass properties. The second part of the dissertation builds on established fixed-wing and rotary-wing aircraft system identification methods to develop modeling strategies for transitioning, distributed propulsion, eVTOL aircraft. Novel wind-tunnel experiment designs and aero-propulsive modeling approaches are developed using a subscale, tandem tilt-wing, eVTOL aircraft, leveraging design of experiments and response surface methodology techniques. Additionally, a method applying orthogonal phase-optimized multisine input excitations to aircraft control effectors in wind-tunnel testing is developed to improve test efficiency and identified model utility. Finally, the culmination of this dissertation is synthesis of the techniques described throughout the document to form a flight-test system identification approach for eVTOL aircraft that is demonstrated using a high-fidelity flight dynamics simulation. The research findings highlighted throughout the dissertation constitute substantial progress in efficient empirical aircraft modeling strategies that are applicable to many current and future aeronautical vehicles enabling accurate flight simulation development, which can subsequently be used to foster advancement in many other pertinent technology areas. / Doctor of Philosophy / Small, electric-powered airplanes flown without an onboard pilot, as well as novel electric aircraft configurations with many propellers that operate at a wide range of speeds, referred to as electric vertical takeoff and landing (eVTOL) aircraft, have recently emerged as aeronautical vehicles able to perform new tasks for future airborne transportation methods. This dissertation presents several mathematical modeling research advancements for these modern aircraft that foster accurate description and prediction of their motion in flight. The mathematical models are developed from data collected in wind-tunnel tests that force air over a vehicle to simulate the aerodynamic forces in flight, as well as from data collected while flying the aircraft. The first part of the dissertation focuses on advances in mathematical modeling approaches using flight data collected from small traditional airplane configurations that are controlled by a pilot operating the vehicle from the ground. A generalized approach for mathematical model development for small airplanes from flight data is described and is followed by presentation of novel modeling applications, including: characterization of the coupled airframe and propulsion aerodynamics and model development when vehicle mass properties are not known. The second part of the dissertation builds on established airplane, helicopter, and multirotor mathematical modeling methods to develop strategies for characterization of the flight motion of eVTOL aircraft. Innovative data collection and modeling approaches using wind-tunnel testing are developed and applied to a subscale eVTOL aircraft with two tilting wings. Statistically rigorous experimentation strategies are employed to allow the effects of many individual controls and their interactions to be simultaneously distinguished while also allowing expeditious test execution and enhancement of the mathematical model prediction capability. Finally, techniques highlighted throughout the dissertation are combined to form a mathematical modeling approach for eVTOL aircraft using flight data, which is demonstrated using a realistic flight simulation. The research findings described throughout the dissertation constitute substantial progress in efficient aircraft modeling strategies that are applicable to many current and future vehicles enabling accurate flight simulator development, which can subsequently be used for many research applications.

System identification is used in engineering sciences to build mathematical models from data. A common issue in system identification problems is that the true inputs to the system are not fully known. In this thesis, existing approaches to unknown input problems are classified and some of their properties are analyzed.  A new indirect framework is proposed to treat system identification problems with unknown inputs. The effects of the unknown inputs are assumed to be measured through possibly unknown dynamics. Furthermore, the measurements may also be dependent on other known or measured inputs and can in these cases be called indirect input measurements. Typically, these indirect input measurements can arise when a subsystem of a larger system is of interest and only a limited set of sensors is available. Two examples are when it is desired to estimate parts of a mechanical system or parts of a dynamic network without full knowledge of the signals in the system. The input measurements can be used to eliminate the unknown inputs from a mathematical model of the system through algebraic manipulations. The resulting indirect model structure only depends on known and measured signals and can be used to estimate the desired dynamics or properties. The effects of using the input measurements are analyzed in terms of identifiability, consistency and variance properties. It is shown that cancelation of shared dynamics can occur and that the resulting estimation problem is similar to errors-in-variables and closed-loop estimation problems because of the noisy inputs used in the model. In fact, the indirect framework unifies a number of already existing system identification problems that are contained as special cases. For completeness, an instrumental variable method is proposed as one possibility for estimating the indirect model. It is shown that multiple datasets can be used to overcome certain identifiability issues and two approaches, the multi-stage and the joint identification approach, are suggested to utilize multiple datasets for estimation of models. Furthermore, the benefits of using the indirect model in filtering and for control synthesis are briefly discussed.  To show the applicability, the framework is applied to the roll dynamics of a ship for tracking of the loading conditions. The roll dynamics is very sensitive to changes in these conditions and a worst-case scenario is that the ship will capsize.  It is assumed that only motion measurements from an inertial measurement unit (IMU) together with measurements of the rudder angle are available. The true inputs are thus not available, but the measurements from the IMU can be used to form an indirect model from a well-established ship model. It is shown that only a subset of the unknown parameters can be estimated simultaneously. Data was collected in experiments with a scale ship model in a basin and the joint identification approach was selected for this application due to the properties of the model. The approach was applied to the collected data and gave promising results. / Till skillnad från många andra industrier där avancerade styrsystem har haft en omfattande utveckling under de senaste decennierna så har styrsystem för skepps- och marinindustrin inte alls utvecklats i samma utsträckning. Det är framförallt under de senaste 10 åren som lagkrav och stigande driftskostnader har ökat intresset för effektivitet och säkerhet genom användning av styrsystem. Rederier och den marina industrin är nu intresserade av hur de avancerade styrsystem som används inom andra områden kan tillämpas för marina ändamål. Huvudmålet är typiskt att minska den totala energianvändningen, och därmed också bränsleförbrukningen, genom att hela tiden planera om hur skeppet skall användas med hjälp av ny information samt styra skeppet och dess ingående system på ett sätt som maximerar effektiviteten. För många av dessa avancerade styrsystem är det grundläggande att ha en god förståelse för beteendet hos det systemet som skall styras. Ofta används matematiska modeller av systemet för detta ändamål. Sådana modeller kan skapas genom att observera hur systemet reagerar på yttre påverkan och använda dessa observationer för att finna eller skatta den modell som bäst beskriver observationerna. Observationerna är mätningar som görs med så kallade sensorer och tekniken att skapa modeller från mätningarna kallas för systemidentifiering. Detta är i grunden ett utmanande problem och det kan försvåras ytterligare om de sensorer som behövs inte finns tillgängliga eller är för dyra att installera. I denna avhandling föreslås en ny teknik där de mätningar som finns tillgängliga används på ett nytt och annorlunda sätt. Detta kan minska mängden nödvändiga sensorer eller möjliggöra användandet av alternativa sensorer i modell-framtagningen. Med hjälp av denna nya teknik kan enkla sensorer användas för att skatta en matematisk modell för en del av skeppet på ett sätt som inte är möjligt med traditionella metoder. Genom att skatta denna modell kan fysikaliska egenskaper hos skeppet, så som dess massa och hur massan är fördelad över skeppet, övervakas för att upptäcka förändringar. Just dessa två egenskaper har stor inverkan på hur skeppet beter sig och om skeppet är fellastat kan det i värsta fall kapsejsa. Vetskapen om dessa fysikaliska egenskaper kan alltså utöver effektivisering användas för att varna besättningen eller påverka styrsystemen så att farliga manövrar undviks. För att visa att tekniken fungerar i verkligheten har den använts på mätningar som har samlats in från ett skalenligt modellskepp. Experimenten utfördes i bassäng och resultaten visar att tekniken fungerar. Denna nya teknik är inte specifik för marint bruk utan kan också vara användbar i andra typer av tillämpningar. Även i dessa tillämpningar möjliggörs användandet av färre eller alternativa sensorer för att skatta modeller. Tekniken kan vara speciellt användbar när en modell av ett system eller process som verkar i ett nätverk av många system är av intresse, något som också diskuteras i avhandlingen.

Airbus réalise pour chaque avion et pour chaque équipement de nombreux essais, au sol ou en vol et doit garantir qu'en tout point de vol possible, la température de chacun des équipements reste inférieure à la température limite correspondante. Pour pouvoir valider la température de chaque équipement dans l'enveloppe de vol, il faudrait disposer d'essais réalisés aux frontières. Or, tous les essais en vol sont confrontés aux contraintes climatiques et opérationnelles qui ne permettent pas d'explorer tout le domaine. C'est pourquoi Airbus a besoin d'élaborer des méthodes d'extrapolation de température, de manière à prédire le comportement thermique des matériaux et des équipements dans les pires conditions. Les techniques proposées sont basées sur la théorie de l'identification de systèmes qui consiste à déterminer des modèles de comportement d'un point de vue heuristique à partir de mesures et considérations physiques. Plus précisément, le présent document valide les modèles ARX comme un outil pour l'identification de la température du système. Les modèles et les techniques sont étudiés, tout d'abord, d'un point de vue de la simulation numérique et après, confrontés face à des tests représentatifs au laboratoire. Les techniques proposées permettent prédire la température des composants avion pour des conditions différentes / Airbus must ensure that for all flight conditions that a given aircraft could face, the temperature of each powerplant system must be less than the corresponding critical temperature. In order to validate the temperature of each device in the flight envelope, tests at the border should be done. Airbus produces for each aircraft component many trials, either in flight or ground. However, all flight tests are faced with climatic and operational constraints which do not permit exploring the whole area. That's why Airbus needs to develop methods of extrapolation of temperature in order to predict the thermal behavior of materials and equipments in the worst conditions. The proposed techniques are based on the system identification theory which consists on heuristically determining an analytical model using physical insights and measurements. More precisely, this paper validates ARX models as a tool for the identification of the system's temperature. The models and techniques are studied, first, from a numerical simulation point of view and second, based on laboratory representative tests. The proposed techniques allow predicting the temperature of aircraft components at different conditions

The interest for marine research and exploration has increased rapidly during the past decades and autonomous underwater vehicles (AUV) have been found useful in an increased amount of applications. The demand for versatile platform AUVs, able to perform a wide range of tasks, has become apparent. A vital part of an AUV is its motion control system, and an emerging problem for multipurpose AUVs is that the control performance is affected when the vehicle is configured with different payloads for each mission. Instead of having to manually re-tune the control system between missions, a method for automatic tuning of the control system has been developed in this master’s thesis. A model-based approach was implemented, where the current vehicle dynamics are identified by performing a sequence of excitation maneuvers, generating informative data. The data is used to estimate model parameters in predetermined model structures, and model-based control design is then used to determine an appropriate tuning of the control system. The performance and potential of the suggested approach were evaluated in simulation examples which show that improved control can be obtained by using the developed auto-tuning method. The results are considered to be sufficiently promising to justify implementation and further testing on a real AUV. The automatic tuning process is performed prior to a mission and is meant to compensate for dynamic changes introduced between separate missions. However, the AUV dynamics might also change during a mission which requires an adaptive control system. By using the developed automatic tuning process as foundation, the first steps towards an indirect adaptive control approach have been suggested. Also, the AUV which was studied in the thesis composed another interesting control problem by being overactuated in yaw control, this because yawing could be achieved by using rudders but also by differential drive of the propellers. As an additional and separate part of the thesis, an approach for using both techniques simultaneously have been proposed.

Aircraft system identification has been widely used to this day in applications like control law design, building simulators or extending flight envelopes. It can also be utilized for determining flight-mechanical characteristics in the preliminary design phase of a flight vehicle. In this thesis, three common time-domain methods were implemented in MATLAB for determining the aerodynamic derivatives of a subscale aircraft. For parameter estimation, the equation-error method is quick, robust and can provide good parameter estimates on its own. The output-error method is computationally intensive but keeps account of the aircraft's evolution in time, being more suitable for fine-tuning predictive models. A new model structure is identified using multivariate orthogonal functions with a predicted squared error stopping criteria. This method is based on linear regression (equation-error). The code written is flexible and can also be used for other aircraft and with other aerodynamic models. Simulations are compared with experimental data from a previous flight test campaign for validation. In the future, this tool may help taking decisions in conceptual design after a prototype is tested.

O estudo das não linearidades das séries temporais da microviga de um microscópio de força atômica tem sido essencial para o desenvolvimento e aperfeiçoamento deste equipamento de ampliação de imagens. Não linearidades podem aparecer com frequência nos experimentos, afetando significativamente a resposta prevista de forma que estas instabilidades se concretizam em imagens ruins. Esta tese apresenta resultados obtidos através de uma abordagem experimental e teórica. Buscou-se aperfeiçoar modelos clássicos de osciladores do microscópio de força atômica melhorando seu comportamento caótico através da observação dos resultados dos experimentos. A identificação de sistemas é feita pelo método de espaço de estados. Outra abordagem de séries temporais obtidas através de um microscópio de força atômica torna possível a reconstrução de espaço de estados, utilizando-se de técnicas como informação mútua, falsos vizinhos e defasagem de tempo. Analisa-se também o comportamento caótico das séries temporais usando o teste 0-1 e escala indexada em quatro experimentos que resultam em um mapa que relaciona a altura que a microviga vibra com o coeficiente do teste 0-1 e com a escala indexada. / The study of time series nonlinearities of the cantilever\'s atomic force microscope has been essential to the development and improvement of this image magnification equipment. Nonlinearities may appear frequently in the experiments, significantly affecting expected response in a way that these instabilities generate bad images. This thesis presents results obtained through an experimental and theoretical approach accordingly. We attempted to improve the classical models of oscillators atomic force microscope improving its chaotic behavior by observing the results of the experiments. The system identification is made by method space state. Another approach to time series obtained through an atomic force microscope makes it possible to reconstruct space phase, using techniques such as mutual information and false neighbors delay. It is analyzed chaotic behavior time series by using the 0-1 test and scale index in four experiments resulting in a map that relates the height of cantilever deflections with the 0-1 test coefficient and the indexed scale.