Vibration-based damage detection (VBDD) comprises a family of non-destructive testing methods in which changes to dynamic characteristics are used to track the condition of a structure. Although VBDD methods have been successfully applied to various mechanical systems and to simple beam-like structures, significant challenges remain in extending this technology to complex, spatially distributed structures such as bridges. <p> In the present study, numerical simulations using a calibrated finite element model were used to investigate the use of VBDD methods to detect small-scale damage on a two-span, integral abutment overpass structure located in Saskatoon, Saskatchewan. The small scale damage was defined in this study as the removal of a concrete element from the top surface of the bridge deck, resembling the spalled clear cover of concrete deck of the overpass. Five different VBDD techniques were evaluated, including the Change in Mode Shape, Change in Flexibility, Change in Mode Shape Curvature, Change in Uniform Flexibility Curvature and Damage index methods. In addition, the influence of the size of damage, the orientation of damage geometry, sensor spacing (3 m, 5 m and 7.5 m), the approach used for mode shape normalization, and uncertainty in the measured mode shapes was investigated. <p> It was found that localized damage could be reliably detected and located if the sensors were located within 3 m of the damage (the distance between adjacent girders) and if uncertainty in the mode shapes was attenuated through the use of a sufficient number of repeated trials. Furthermore, studies using a limited sensor installation that could be achieved without interrupting the flow of traffic indicated that small scale damage could be detected and potentially located using sensors that are placed well away from the damaged area, provided uncertainty in mode shape was attenuated.

Vibration-based damage detection (VBDD) comprises a family of non-destructive testing methods in which changes to dynamic characteristics are used to track the condition of a structure. Although VBDD methods have been successfully applied to various mechanical systems and to simple beam-like structures, significant challenges remain in extending this technology to complex, spatially distributed structures such as bridges. <p> In the present study, numerical simulations using a calibrated finite element model were used to investigate the use of VBDD methods to detect small-scale damage on a two-span, integral abutment overpass structure located in Saskatoon, Saskatchewan. The small scale damage was defined in this study as the removal of a concrete element from the top surface of the bridge deck, resembling the spalled clear cover of concrete deck of the overpass. Five different VBDD techniques were evaluated, including the Change in Mode Shape, Change in Flexibility, Change in Mode Shape Curvature, Change in Uniform Flexibility Curvature and Damage index methods. In addition, the influence of the size of damage, the orientation of damage geometry, sensor spacing (3 m, 5 m and 7.5 m), the approach used for mode shape normalization, and uncertainty in the measured mode shapes was investigated. <p> It was found that localized damage could be reliably detected and located if the sensors were located within 3 m of the damage (the distance between adjacent girders) and if uncertainty in the mode shapes was attenuated through the use of a sufficient number of repeated trials. Furthermore, studies using a limited sensor installation that could be achieved without interrupting the flow of traffic indicated that small scale damage could be detected and potentially located using sensors that are placed well away from the damaged area, provided uncertainty in mode shape was attenuated.

The steel through-truss Traffic Bridge, located in Saskatoon, Saskatchewan is over one hundred years old. The bridge has been subject to ongoing maintenance throughout its service life. However, inspection reports from 2005 and 2006 highlighted the severe deterioration experienced primarily by the steel members immediately above and below the deck surface. These reports prompted the City of Saskatoon (COS) to implement a rehabilitation project that involved the installation of a post-tensioning system to relieve the badly corroded bottom chord members of the axial loads due to the self-weight of the structure, in 2006. Due to the severe deterioration and the structural modifications that the Traffic Bridge has endured, a limited scope structural health monitoring (SHM) system, based on strain measurements, was implemented to reduce some of the uncertainty regarding the active load paths occurring at the deck level. The objectives of the SHM study were to obtain more information regarding the actual load paths and ascertain possible types of structural redundancy, to determine how to best model this type of structure, and to find ways to track ongoing deterioration using instrumentation. The SHM study involved controlled truck loading scenarios to permit measurement of the load paths and provide data to compare the measured results to a finite element (FE) model of the instrumented span. In addition, random loading scenarios were used to capture the vertical dynamic response of the structure in order to further refine the FE model. This study focused on the response of one-half of one interior span. A total of 72 strain gauges were installed. The downstream truss was highly instrumented at ten locations, three members of the upstream truss were instrumented to measure the distribution, and the floor joists in the downstream lane were instrumented to establish possible redundancy paths. Using an FE model in combination with the measured strain data, it was found that redundant load paths only existed at the level of the deck. The bottom chord members experienced non-zero strains once the control vehicle was past the span, possibly indicating some level of redundancy. The members believed to relieve a portion of the bottom chord tensile forces included the car joists, edge joists, and the timber deck. The amount of force transferred from the bottom chord to the deck members was found by FE analysis to be highly related to the lateral stiffness of the floor beams. The FE model was adjusted to match the measured results by modifying various modelling parameters. The most important features of the model were that all deck elements were modelled to be located at the elevation of the bottom chord, that the lateral stiffness of the floor beams was reduced by 50% to best represent the transfer of forces to deck elements, and that the stiffness of bottom chord members was reduced to 80% of their pristine values. In combination with calibrated modification factors applied to the measured values, this FE model is believed to be a useful tool to represent the behaviour of the structure to assist in detecting further damage by modelling the strain differential between members, and components of members.

Structural Health Monitoring (SHM) refers to the process of nondestructive autonomous in situ monitoring of the integrity of critical engineering structures such as airplanes, bridges and buildings. Ultrasonic wave propagation is an ideal interrogation method for SHM because ultrasound is the elastic vibration of the material itself and is thus directly affected by any structural damage occurring in the paths of the propagating waves. The objective of this thesis is to provide a comprehensive damage detection strategy for SHM using diffuse ultrasonic waves. This strategy includes a systematic temperature compensation method, differential feature extraction methods optimized for discriminating benign surface condition changes from damage, and data fusion methods to determine the structural status. The temperature compensation method is based upon a set of pre-recorded baselines. Using the methods of baseline selection and baseline correction, a baseline that best matches a monitored signal in temperature is provided. For the differential feature extraction, three types of features are proposed. The first type includes basic differential features such as mean squared error. The second type is derived from a matching pursuit based signal decomposition. An ultrasonic signal is decomposed into a sum of characteristic wavelets, and differential features are extracted based upon changes in the decomposition between a baseline signal and a monitored signal. The third type is a phase space feature extraction method, where an ultrasonic signal is embedded into phase space and features are extracted based on changes of the phase portrait. The structural status is determined based on a data fusion strategy consisting of a threshold selection method, fusion at the feature level, and fusion at the sensor level. The proposed damage detection strategy is applied to experiments on aluminum specimens with artificial defects subjected to a variety of environmental variations. Results as measured by the probability of detection, the false alarm rate, and the size of damage detected demonstrate the viability of the proposed techniques.

A new statistical algorithm, the "modal distribution method", is proposed to statistically quantify the significance of changes in mean frequencies of individual modal vibrations of measured structural response data. In this new method, a power spectrum of measured structural response is interpreted as being a series of independent modal responses, each of which is isolated over a frequency range and treated as a statistical distribution. Pairs of corresponding individual modal distributions from different segments are compared statistically. The first version is the parametric MDM. This method is applicable to well- separated modes having Gaussian shape. For application to situations in which the signal is corrupted by noise, a new noise reduction methodology is developed and implemented. Finally, a non-parametric version of the MDM based on the Central Limit Theorem is proposed for application of MDM to general cases including closely spaced peaks and high noise. Results from all three MDMs are compared through application to simulated clean signals and the two extended MDMs are compared through application to simulated noisy signals. As expected, the original parametric MDM is found to have the best performance if underlying requirements are met: signals that are clean and have well-separated Gaussian mode shapes. In application of nonparametric methods to Gaussian modes with high noise corruption, the noise reduction MDM is found to have lower probability of false alarms than the nonparametric MDM, though the nonparametric is more efficient at detecting changes. In closely related work, the Hermite moment model is extended to highly skewed data. The aim is to enable transformation from non-Gaussian modes to Gaussian modes, which would provide the possibility of applying parametric MDM to well- separated non-Gaussian modes. A new methodology to combine statistical moments using a histogram is also developed for reliable continuous monitoring by means of MDM. The MDM is a general statistical method. Because of its general nature, it may find a broad variety of applications, but it seems particularly well suited to structural health monitoring applications because only very limited knowledge of the excitation is required, and significant changes in computed power spectra may indicate changes, such as structural damage.

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method, an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared numerically and experimentally using measurements from two structures, a simply supported beam and a pedestrian bridge. The dynamic properties for the numerical comparison are extracted by modal analysis in ABAQUS, while the dynamic characteristics for the experimental comparison are obtained with the Wireless Sensor Network and the Time Domain Decomposition. In both the numerical and experimental phases, the accuracy of damage predictions from each method is quantified. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate when applied to both structures. The proposed IDIM has the potential to improve SHM.

Structural health monitoring (SHM) is an active research area devoted to the assessment of the structural integrity of critical components of aerospace, civil and mechanical systems. Guided wave methods have been proposed for SHM of plate-like structures using permanently attached piezoelectric transducers, which generate and sense waves to evaluate the presence of damage. Effective interrogation of structural health is often facilitated by sensors and actuators with the ability to perform directional scanning. In this research, the novel class of Frequency Steerable Acoustic Transducers (FSATs) is proposed for directional generation/sensing of guided waves. The FSATs are characterized by a spatial arrangement of the piezoelectric material which leads to frequency-dependent directionality. The resulting FSATs can be employed both for directional sensing and generation of guided waves, without relying on phasing and control of a large number of channels. Because there is no need for individual control of transducer elements, hardware and power requirements are drastically reduced so that cost and hardware limitations of traditional phased arrays can be partially overcome. The FSATs can be also good candidates for remote sensing and actuation applications, due to their hardware simplicity and robustness. Validation of the proposed concepts first employs numerical methods. Next, the prototyping of the FSATs allows an experimental investigation confirming the analytical and numerical predictions. Imaging algorithm based on frequency warping is also proposed to enhance results representation.

<p>Structural Health Monitoring (SHM) is a helpful tool for engineers in order to control and verify the structural behaviour. SHM also guides the engineers and owners of structures in decision making concerning the maintenance, economy and safety of structures. Sweden has not a very sever tradition in monitoring, as countries with strong seismic and/or aerodynamic activities. Anyway, several large scale monitoring projects have taken place in recent years and SHM is slowly making entrance as an essential implement in managing structures by engineers as well as owners.</p><p>This licentiate thesis presents a state-of-the art-review of health monitoring activities and over sensory technologies for monitoring infrastructure constructions like bridges, dams, off-shore platforms, historical monuments etc. related to civil engineering. The fibre optic equipment is presented with special consideration.</p><p>The permanent monitoring system of the New Årsta Bridge consists of 40 fibre optic sensors, 20 strain transducers, 9 thermocouples, 6 accelerometers and one LVDT. The aims of the static study are: to control the maximal strains and stresses; to detect cracking in the structure; to report strain changes under construction, testing period and in the coming 10 years; and to compare conventional system with fibre optic system.</p><p>The system installation started in January 2003 and was completed October 2003. The measurements took place from the very beginning and are suppose to continue for at least 10 years of operation. At the construction phase the measurements were performed manually and later on automatically through broad band connection between the office and central data acquisition systems located inside the bridge.</p><p>The monitoring project of the New Årsta Railway Bridge is described from the construction phase to the testing phase of the finished bridge. Results of the recorded statistical data, crack detection and loading test are presented and a comparison between traditional techniques like strain transducers and fibre optic sensors is done.</p><p>Various subjects around monitoring and sensor technologies that were found under the project are brought up in order to give the reader a good understanding, as well of the topics, techniques and of the bridge. Example of few applications is given with the aim of a deeper insight into monitoring related issues.</p>

The research reported in this thesis is part of a project to develop a remote wireless sensing network for monitoring the health of highway bridges. Remote health monitoring that does not require direct human observation has many advantages in terms of cost and increased productivity. However, bridges that cannot be easily connected to the power grid require alternative means of acquiring power. This thesis describes the design of a wind energy harvester to power a particular component in the sensor network, the wireless router. The work discussed in this thesis provides a review of relevant literature and development of a detailed analytical modeling of wind turbine behavior. The analytical model provides key information on sizing generators and choosing appropriate wind turbine dimensions to provide the required amount of power. The analytical model also distinguishes the performance of vertical and horizontal axis wind turbines. The model is verified through design and testing of a first generation prototype and benchmarking of a commercially available turbine. Based on these results, the design of the next generation wind harvesting system is described. A new methodology to design non-destructive attachment systems is also discussed. / text

Predicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab. / text