In den letzten Jahrzehnten ist die Komplexität und Vernetzung einzelner Komponenten im Automobil stetig gewachsen. Der Grund dafür sind immer zahlreicher werdende Systeme im und rund um das Auto, welche das tägliche Leben sicherer und komfortabler machen. Von ABS über elektronische Motorsteuerungen bis hin zu autonom fahrenden Fahrzeugen werden in den nächsten Jahren immer aufwendigere Systeme zum Einsatz kommen. Es wird davon ausgegangen, dass in Zukunft 90% der Innovationen im Fahrzeug von der programmierbaren Elektronik geprägt sein werden, was die Komplexität der Software in einem enormen Maße beeinussen wird. Schon heute wird versucht durch entsprechende Techniken den Entwicklungsaufwand gering zu halten, da der internationale Konkurrenz- und Kostendruck die Industrie dazu zwingt.

The aerospace industry endeavours to improve modern aircraft capabilities in efficiency, endurance, and comfort. One means of achieving these goals is through new enhancements in aerodynamics. Increased wing aspect ratio is an example of further improving efficiency. However, this comes with new challenges including possibly adverse aero-elastic and aero-servo-elastic (ASE) phenomena. New computational methods and tools are emerging and there is a need for experimental data for validation. University of Victoria’s Centre for Aerospace Research (UVic CfAR) set out to design a 20kg ASE demonstrator using a remotely piloted aircraft (RPA). This aircraft was designed with the intent of exploring coupling between aero-elastic modes including coupling between the short period aerodynamic mode and the first out-of-plane elastic mode of the wing. This thesis discuses the implementation of instrumentation designed and integrated into the ASE RPA demonstrator to monitor the deformation of the elastic wing in-flight. A strain based measurement technique was selected for integration into the ASE aircraft. This choice was made for several reasons including its reliability regardless of outdoor lighting, relatively lightweight processing requirements for real time applications, and suitable sampling bandwidth. To compute the wing deformation from strain, a method, sometimes referred to as strain pattern analysis (SPA), utilizing linear combinations of reference modal shapes fit against the measured strain, was used. Although this method is not new, to the author’s knowledge, it is the first practical application to a reduced scale RPA demonstrator. The deformation measurement system was validated against a series of distributed static load tests on the ground. Distributed load cases along the wing demonstrated good out-of-plane measurement performance. A case where only load is applied near the root of the wing resulted in the largest error in part as the mode shapes generated are less suited to approximate the resulting shape. In general errors in out-of-plane displacement at the end of the flexible wing portion can be expected to be less than 5%. The displacement at the tip of the wing can be as great as 11% for the left wing whereas the right wing is 4.7%. This suggest an asymmetry between the left and right wings requiring specifically tuned FE models for each to achieve best results. Twist angles presented in tests were relatively small for accurate comparison against the reference measurement, which was relatively noisy. Generally, the deformation measurement by SPA technique followed the same twist behaviours as the reference. A twist case, unlikely to be seen in flight, provided some insight into twist measurement robustness. The work presented is merely a small step forward with many opportunities for further research. There is room for improvement of the FE model used to generate the mode shapes in the strain pattern analysis. Initial efforts focused on the flexible spar portion of the wing. With more work improvements could be achieved for the estimation of the rigid wing. Additionally, there was some asymmetry between each wing semi-span, and with some focus on the left wing its results could be improved to at least match that of the right wing. A real-time implementation was not completed and would be particularly interesting for use as feedback for flight control. Study of load alleviation techniques may benefit. Another topic of study is the combination of this method with other measurements, such as accelerometers, to provide improved performance state estimation through sensor fusion. / Graduate

Structural Health monitoring is to determine the condition of a bridge based on instrument measurements. The C-846 Bridge in Salt Lake City has such instrumentation. The bridge is located in Salt Lake City at about 2100 South and Interstate 15. This bridge has two kinds of instruments on it: accelerometers and thermocouples. The accelerometers measure the vibrations of the bridge. The accelerometers have been recording data on the bridge since 2001. The thermocouples, which measure temperature, were added as part of this thesis in April 2016. In light of recent research, damage may be detected from measuring the change in the natural frequency of a bridge, which can be obtained by manipulating the accelerometer data. However, the natural frequencies of a bridge change due to environmental effects, especially temperature. Temperature effects must be accounted for in order to better understand the damage. The purpose of this research is not to detect damage. The bridge that is being monitored does not have any damage. The purpose of this study is to show how the dynamic properties of the C-846 Bridge in South Salt Lake City correlate with temperature. Additionally, several frequencies including the fundamental frequency of the bridge are identified. It was found that the natural frequencies of the bridge increase with a decrease in temperature, and that the fundamental frequency of the bridge is 1.15 Hz.

This dissertation presents a novel procedure to select explanatory variables, without the influence of human bias, for deterioration model development using National Bridge Inventory (NBI) data. Using NBI information, including geometric data and climate information, candidate explanatory variables can be converted into normalized numeric values and analyzed prior to the development of deterministic or stochastic deterioration models. The prevailing approach for explanatory variable selection is to use expert opinion solicited from experienced engineers. This may introduce human influenced biases into the deterioration modeling process. A framework using Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression and covariance analysis are combined to compensate for this potential bias. Additionally, the cross validation analysis and solution path is used as a standard for the selection of minimum number of explanatory variables. The proposed method is demonstrated through the creation of deterministic deterioration models for deck, superstructure, and substructure for Wyoming bridges and compared to explanatory variables using the expert selection method. The comparison shows a significant decrease in error using the presented framework based on the L2 relative error norm. The final chapter presents a new method to develop stochastic deterioration models using logistic regression. The relative importance amongst explanatory variables is used to develop a classification tree for Wyoming bridges. The bridges in a subset are commonly associated with several explanatory variables, so that the deterioration models can be more representative and accurate than using a single explanatory variable. The logistic regression is used to introduce the stochastic contribution into the deterioration models. In order to avoid missing data problems, the binary categories condition rating, either remaining the same or decreased, are considered for logistic regression. The probability of changes in bridges’ condition rating is obtained and the averages for same condition ratings are used to create transition probability matrix for each age group. The deterioration model based on Markov chain are developed for Wyoming bridges and compared with the previous model based on percentage prediction and optimization approach. The prediction error is analyzed, which demonstrates the considerable performance of the proposed method and is suitable for relatively small data samples.

Depuis plusieurs années l'équipe de recherche S4M se concentre sur une approche technologique de la SHM avec pour objectif la surveillance de systèmes complexes par des capteurs intelligents distribués: le Smart Sensing. L'équipe S4M conduit des travaux d'instrumentation de structures complexes au travers du déploiement de systèmes de surveillance distribués et de recherche de marqueurs de vieillissement par la mesure et l'exploitation de signaux via des capteurs MEMS déployés. Différents domaines ont déjà été adressés avec des travaux conduits conjointement avec des constructeurs aéronautiques. Ce travail de recherche, effectué en partenariat avec le laboratoire LMDC de l'INSA se focalise sur le matériau de type béton renforcé par des plaques composites, comme structure hétérogène nécessitant une surveillance périodique et/ou continue. Un des enjeux est de contrôler la maintenance préventive ou le surdimensionnement par des coefficients de confiance en proposant une méthode de contrôle non destructif. Notre objectif de recherche est de contribuer dans la recherche d'une ou de signature(s) dans des signaux mesurés par des capteurs piezo en réponse à des impulsions générant la propagation d'ondes mécaniques témoignant un vieillissement ou un endommagement de la structure poutre en béton armé. / For several years the research team S4M focuses on a technological approach to SHM with the aim for monitoring of complex systems by intelligent sensors distributed: Smart Sensing. The S4M team led instrumentation complex structures work through the deployment of distributed monitoring systems and search for markers of aging by measuring and operating signals through deployed MEMS sensors. Different areas have already been addressed with the work conducted jointly with aircraft manufacturers. This research, conducted in partnership with the LMDC-INSA laboratory focuses on the concrete like material reinforced composite plates as heterogeneous structure requiring periodic or continuous monitoring. One of the challenges is to control preventive maintenance or oversizing trusted coefficients by providing a non-destructive testing method. Our research goal is to help in the search for a signature in the signals measured by piezo sensors in response to pulses generating propagation of mechanical waves reflecting an aging or damage to the beam structure of reinforced concrete.

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Wave propagation analysis in 1-D and 2-D composite structures is performed efficiently and accurately through the formulation of a User-Defined Element (UEL) based on the wavelet spectral finite element (WSFE) method. The WSFE method is based on the first order shear deformation theory which yields accurate results for wave motion at high frequencies. The wave equations are reduced to ordinary differential equations using Daubechies compactly supported, orthonormal, wavelet scaling functions for approximations in time and one spatial dimension. The 1-D and 2-D WSFE models are highly efficient computationally and provide a direct relationship between system input and output in the frequency domain. The UEL is formulated and implemented in Abaqus for wave propagation analysis in composite structures with complexities. Frequency domain formulation of WSFE leads to complex valued parameters, which are decoupled into real and imaginary parts and presented to Abaqus as real values. The final solution is obtained by forming a complex value using the real number solutions given by Abaqus. Several numerical examples are presented here for 1-D and 2-D composite waveguides. Wave motions predicted by the developed UEL correlate very well with Abaqus simulations using shear flexible elements. The results also show that the UEL largely retains computational efficiency of the WSFE method and extends its ability to model complex features. An enhanced cross-correlation method (ECCM) is developed in order to accurately predict damage location in plates. Three major modifications are proposed to the widely used cross-correlation method (CCM) to improve damage localization capabilities, namely actuator-sensor configuration, signal pre-processing method, and signal post-processing method. The ECCM is investigated numerically (FEM simulation) and experimentally. Experimental investigations for damage detection employ a PZT transducer as actuator and laser Doppler vibrometer as sensor. Both numerical and experimental results show that the developed method is capable of damage localization with high precision. Further, ECCM is used to detect and localize debonding in a composite material skin-stiffener joint. The UEL is used to represent the healthy case whereas the damaged case is simulated using Abaqus. It is shown that the ECCM successfully detects the location of the debond in the skin-stiffener joint.

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No description available.

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