This thesis describes the development of a measurement system for monitoring dynamic tests of civil engineering structures using long exposure motion blurred images, named LEMBI monitoring. Photogrammetry has in the past been used to monitor the static properties of laboratory samples and full-scale structures using multiple image sensors. Detecting vibrations during dynamic structural tests conventionally depends on high-speed cameras, often resulting in lower image resolutions and reduced accuracy. To overcome this limitation, the novel and radically different approach presented in this thesis has been established to take measurements from blurred images in long-exposure photos. The motion of the structure is captured in an individual motion-blurred image, alleviating the dependence on imaging speed. A bespoke algorithm is devised to determine the motion amplitude and direction of each measurement point. Utilising photogrammetric techniques, a model structure s motion with respect to different excitations is captured and its vibration envelope recreated in 3D, using the methodology developed in this thesis. The approach is tested and used to identify changes in the model s vibration response, which in turn can be related to the presence of damage or any other structural modification. The approach is also demonstrated by recording the vibration envelope of larger case studies in 2D, which includes a full-scale bridge structure, confirming the relevance of the proposed measurement approach to real civil engineering case studies. This thesis then assesses the accuracy of the measurement approach in controlled motion tests. Considerations in the design of a survey using the LEMBI approach are discussed and limitations are described. The implications of the newly developed monitoring approach to structural testing are reviewed.

Ces travaux s’inscrivent dans le cadre du projet ANR "CAPTIF" (CAPTeurs innovants Intégrés et logiciels au coeur d’un dispositif d’électronique de puissance). Ce projet s’intéresse aux solutions de suivi de vieillissement en temps réel de modules d’électronique de puissance pour réduire les coûts de maintenance et augmenter la fiabilité des systèmes de conversion d’énergie.Les limitations des modèles de fiabilité actuels sont mises en évidence et conduisent à la recherche d’une approche plus représentative des mécanismes entraînant la défaillance des modules de puissance et à une remise en question du traditionnel nombre de cycles moyens avant défaillance en tant qu’outil de gestion de la fiabilité. À partir de l’identification des mécanismes de vieillissement des assemblages de puissance, une nouvelle méthodologie de modélisation est proposée pour caractériser la durée de vie résiduelle d’un module, en s’appuyant sur un indicateur d’endommagement énergétique. La fonction de corrélation entre cet indicateur de vieillissement et les données issues de capteurs embarqués est alors construite à l’aide de méthodes de régression numérique. Une mise en application sur un assemblage représentatif illustre l’intérêt de la méthodologie. / This work is part of the french Research National Agency project called "CAPTIF" (meaning"Innovative embedded sensors and software for power electronics devices"). This research project deals with power electronics health monitoring solutions in order to increase electric conversion devices reliability and decrease maintenance costs.Traditionnal reliability models limitations led to a reconsideration of life prediction methodsand mean time to failure as a reliability management tool for power electronics devices.An investigation to develop an approach more representative of failure mechanisms observed in power electronics devices was conducted. Based on ageing mechanisms of power electronics assemblies, an ageing indicator was proposed and used with a modelling methodology to characterise the residual life of a device exposed to thermal loads. The functional link between this ageing indicator and data provided by embedded sensors is approache dusing a numerical design of experiment and a response surface methodology. This modelling process is illustrated by an application to a simplified power assembly.

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles.

Structural health monitoring (SHM) systems have been technologically advancing over the past few years. Improvements in fabrication and microelectronics allow the development of highly sophisticated sensor arrays, capable of detecting and transmitting an unprecedented amount of data. As the complexity of the hardware increases, research has been performed in developing the means to best utilize and effectively process the data. Algorithms from other computational fields are being introduced for the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immune systems (AIS) show great potential. In this thesis, features are extracted out of the acceleration data with the use of discrete wavelet transforms (DWT)s first. The DWT coefficients are used to calculate energy ratios, which are then classified using a neural network and an AIS algorithm known as negative selection (NS). The effectiveness of both methods are validated using simulated acceleration data of a four story structure exhibiting various damage states via computer simulation.

<div>Structural health monitoring (SHM) has immense potential to improve the safety of aerospace, mechanical, and civil structures because it allows for continuous, real-time damage prognostication. However, conventional SHM methodologies are limited by factors such as the need for extensive external sensor arrays, inadequate sensitivity to small-sized damage, and poor spatial damage localization. As such, widespread implementation of SHM in engineering structures has been severely restricted. These limitations can be overcome through the use of multi-functional materials with intrinsic self-sensing capabilities. In this area, composite materials with nanofiller-modified polymer matrices have received considerable research interest. The electrical conductivity of these materials is affected by mechanical stimuli such as strain and damage. This is known as the piezoresistive effect and it has been leveraged extensively for SHM in self-sensing materials. However, prevailing conductivity-based SHM modalities suffer from two critical limitations. The first limitation is that the mechanical state of the structure must be indirectly inferred from conductivity changes. Since conductivity is not a structurally relevant property, it would be much more beneficial to know the displacements, strains, and stresses as these can be used to predict the onset of damage and failure. The second limitation is that the precise shape and size of damage cannot be accurately determined from conductivity changes. From a SHM point of view, knowing the precise shape and size of damage would greatly aid in-service inspection and nondestructive evaluation (NDE) of safety-critical structures. The underlying cause of these limitations is that recovering precise mechanics from conductivity presents an under determined and multi-modal inverse problem. Therefore, commonly used inversion schemes such as gradient-based optimization methods fail to produce physically meaningful solutions. Instead, metaheuristic search algorithms must be used in conjunction with physics-based damage models and realistic constraints on the solution search space. To that end, the overarching goal of this research is to address the limitations of conductivity-based SHM by developing metaheuristic algorithm-enabled methodologies for recovering precise mechanics from conductivity changes in self-sensing composites.</div><div><div><br></div><div>Three major scholarly contributions are made in this thesis. First, a piezoresistive inversion methodology is developed for recovering displacements, strains, and stresses in an elastically deformed self-sensing composite based on observed conductivity changes. For this, a genetic algorithm (GA) is integrated with an analytical piezoresistivity model and physics-based constraints on the search space. Using a simple stress based failure criterion, it is demonstrated that this approach can be used to accurately predict material failure. Second, the feasibility of using other widely used metaheuristic algorithms for piezoresistive inversion is explored. Specifically, simulated annealing (SA) and particle swarm optimization (PSO) are used and their performances are compared to the performance of the GA. It is concluded that while SA and PSO can certainly be used to solve the piezoresistive inversion problem, the GA is the best algorithm based on solution accuracy, consistency, and efficiency. Third, a novel methodology is developed for precisely determining damage shape and size from observed conductivity changes in self-sensing composites. For this, a GA is integrated with physics-based geometric models for damage and suitable constraints on the search space. By considering two specific damage modes —through-holes and delaminations —it is shown that this method can be used to precisely reconstruct the shape and size of damage. </div><div><br></div><div>In achieving these goals, this thesis advances the state of the art by addressing critical limitations of conductivity-based SHM. The methodologies developed herein can enable unprecedented NDE capabilities by providing real-time information about the precise mechanical state (displacements, strains, and stresses) and damage shape in self-sensing composites. This has incredible potential to improve the safety of structures in a myriad of engineering venues.</div></div>

Research in damage detection and structural health monitoring in engineering systems during their service life has received increasing attention because of its importance and benefits in maintenance and rehabilitation of structure. Though the concept of vibration-based damage detection has been in existence for decades, and several procedures have been proposed to date, its practical applications remain limited, considering the increased utilization of sensors to measure structural response at multiple points. In this thesis, use of acceleration response of the structure as a method of global damage detection is explored using instantaneous frequency and stiffness degradation methods. Instantaneous frequency was estimated using continuous wavelet transform of measured acceleration response of the structure subjected to ground motion. Complex Morlet Wavelet was used in the time-frequency analysis due to its ability to provide sufficient resolution in both time and frequency domains. This ability is important in analyzing nonstationary signals like earthquake response of structure containing sharp changes in the signal. The second method, called the stiffness degradation analysis, is based on estimating the time-varying stiffness. This estimation is done by fitting a moving least-square line to the force-displacement loop for the duration of the ground motion.A four-story shear building is used as the model structure for numerical analysis. Two damage scenarios are considered: single damage instant and multiple damage instants. Both scenarios assume that the damage occurs at a single location. In the numerical simulations, damage was modeled as a reduction in the stiffness of the first floor, and accelerations were computed at floor levels using state-space model. The two methods were compared in terms of their damage detection ability and it was shown that both methods can be used in detecting damage and the time at which the damage occurs. These methods can later be extended by simultaneously considering the correlations of responses at all floor levels. This extension may enable locating the damage and quantifying the severity of the damage.

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.

This study examined privacy legislation in California to identify those electronic Protected Health Information (e-PHI) privacy policies that are suited to seniors using in-home health monitoring systems. Personal freedom and independence are essential to a person's physical and mental health, and mobile technology applications provide a convenient and economical method for monitoring personal health. Many of these apps are written by third parties, however, which poses serious risks to patient privacy. Current federal regulations only cover applications and systems developed for use by covered entities and their business partners. As a result, the responsibility for protecting the privacy of the individual using health monitoring apps obtained from the open market falls squarely on the states. The goal of this study was to conduct an exploratory study of existing legislation to learn what was being done at the legislative level to protect the security and privacy of users using in-home mobile health monitoring systems. Specifically, those developed and maintained by organizations or individuals not classified as covered entities under the Health Insurance Portability and Accountability Act of 1996 (HIPAA). The researcher chose California due to its reputation for groundbreaking privacy laws and high population of seniors. The researcher conducted a content analysis of California state legislation, federal and industry best practices, and extant literature to identify current and proposed legislation regarding the protection of e-PHI data of those using in-home health monitoring systems. The results revealed that in-home health monitoring systems show promise, but they are not without risk. The use of smartphones, home networks, and downloadable apps puts patient privacy at risk, and combining systems that were not initially intended to function together carries additional concerns. Factors such as different privacy-protection profiles, opt-in/opt-out defaults, and privacy policies that are difficult to read or are not adhered to by the application also put user data at risk. While this examination showed that there is legislative support governing the development of the technology of individual components of the in-home health monitoring systems, it appears that the in-home health monitoring system as a whole is an immature technology and not in wide enough use to warrant legislative attention. In addition – unlike the challenges posed by the development and maintenance of the technology of in-home health monitoring systems – there is ample legislation to protect user privacy in mobile in-home health monitoring systems developed and maintained by those not classified as covered entities under HIPAA. Indeed, the volume of privacy law covering the individual components of the system is sufficient to ensure that the privacy of the system as a whole would not be compromised if deployed as suggested in this study. Furthermore, the legislation evaluated over the course of this study demonstrated consistent balance between technical, theoretical, and legal stakeholders. This study contributes to the body of knowledge in this area by conducting an in-depth review of current and proposed legislation in the state of California for the past five years. The results will help provide future direction for researchers and developers as they struggle to meet the current and future needs of patients using this technology as it matures. There are practical applications for this study as well. The seven themes identified during this study can serve as a valuable starting point for state legislators to evaluate existing and proposed legislation within the context of medical data to identify the need for legislation to assist in protecting user data against fraud, identity theft, and other damaging consequences that occur because of a data breach.