Browsing by Subject "Structural Health Monitoring"
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Item Iterative Damage Index Method for Structural Health Monitoring(2011-02-22) You, TaesunStructural 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.Item LMS-based method for damage detection applied to Phase II of Structural Health Monitoring benchmark problem(Texas A&M University, 2006-08-16) Preston, Robin HuckabyStructural Health Monitoring (SHM) is the process of monitoring the state of a structure to determine the existence, location, and degree of damage that may exist within the entire structure. A structure??s health or level of damage can be monitored by identifying changes in structural or modal parameters. In this research, the structure??s health is monitored by identifying changes in structural stiffness. The Adaptive Least Mean Square (LMS) filtering approach is used to directly identify changes in structural stiffness for the IASC-ASCE Structural Health Monitoring Task Group Benchmark problem for both Phase I and II. The research focuses primarily on Phase II of the benchmark problem. In Phase II, modeling error and noise is introduced to the problem making the problem more realistic. The research found that the LMS filter approach can be used to detect damage and distinguish relative severity of the damage in Phase II of the benchmark problem in real time. Even though the LMS filter approach identified damage, a threshold below which damage is hard to identify exists. If the overall stiffness changes less than 10%, then identifying the presence and location of damage is difficult. But if the time of damage is known, then the presence and location can be determined. The research is of great interest to those in the structural health monitoring community, structural engineers, and inspection practitioners who deal with structural damage identification problems.Item Methods to Improve Process Safety Performance through Flange Connection Leak Prediction and Control(2014-08-08) Nelson, JeremyProcess safety is a task of preventing leaks. Leak prevention is critical because pressure vessels and piping assets in chemical plants are fabricated from materials which have limited corrosion resistance. When corrosive compounds are processed in these assets, they may suffer degradation over time due to thinning, cracking, or loss of their material properties. This problem is partially controlled by applying a safety margin known called a corrosion allowance. The corrosion allowance is determined by predicting the asset?s expected corrosion rate and its service life. However, this fixed safety margin does not consider the inherent uncertainty in an individual asset?s degradation rate due to variability in the material?s corrosion resistance, the operating parameters of the process, and the inspection techniques used to measure the progression of corrosion damage over time. Consequently, deterministic analysis is not capable of precisely estimating an asset?s safe operating life during its design stage. One of the most likely areas for leakage to occur in process equipment is at the flange connections that join assets together. Risk analyses for planning inspections of fixed equipment and piping usually treat flanges as components of their parent asset. This thesis focuses on methods to improve prediction and control of corrosion and leakage at flange connections in particular. Flange connection seal tightness can be monitored through vibration-based Non-Destruction Testing (NDT). The data gathered from this monitoring can be used to update risk models for flange connection leakage. Hierarchical Bayesian Network methods of modeling risk are demonstrated in this thesis to be capable of predicting probability of seal failure based on the mean and variance of failure rates in a population of flange connections. This allows for prediction of the probabilities based on corrosion and leak events in the plant. The results of inspection techniques are used as inputs to this risk model, enabling probabilistic decision-making.Item Nondestructive Damage Detection in General Beams(2010-12-08) Dincal, SelcukMonitoring the integrity of civil engineering structures is an imperative aspect of public safety, since structural failures can pose serious threats to life and property. Periodic inspection performed throughout the life span of these structures is also vital for a nation?s economy. Substantial sums of money may be saved upon detecting structural deterioration in a timely manner. Nondestructive damage evaluation (NDE) offers effective and economically feasible solutions to perform such tasks. Better predictions can be made regarding the current state of structures, and structurally deficient regions that need immediate attention may successfully be narrowed down by utilizing NDE. For these reasons, a considerable amount of research has been conducted in the field of NDE over the past few decades. As a result, many different methodologies are now available, and many new ones continue to emerge as the need for better evaluation techniques prevails. Upon reviewing the NDE methodologies proposed to date, it may be concluded that theories based on the fundamental equations of mechanics and mathematics in conjunction with justifiable assumptions provided the best results compared to the algorithms developed pragmatically. The goal of this study is to provide NDE methodologies that simultaneously identify the location, the extent, and the severity of damage in general beams. By general beams, we mean beyond Euler-Bernoulli beams (i.e. slender beams) to deep beams and stubby beams whose response may be based on the Timoshenko beam theory, and the Theory of Elasticity. After presenting the governing equations of equilibrium and stress-displacement relations of the fundamental beam theories including the Euler-Bernoulli Beam theory, the Timoshenko beam theory, and the beam theory based on linear Elasticity Theory, mathematical expressions which relate physical properties (e.g. stiffness) of the undamaged and damaged structure to measurable response quantities (e.g. displacement, strains, etc.) are developed. We believe that these algorithms will lead to earlier and more accurate prediction of damage in critical structures. The findings of this work will also lead to a better understanding of the limitations of the currently proposed NDE techniques. In addition, it is anticipated that by incorporating the methodologies proposed in this study to the continuous health monitoring of structural systems could reduce the cost of maintenance and offer safer infrastructure networks.Item THE MODAL DISTRIBUTION METHOD: A NEW STATISTICAL ALGORITHM FOR ANALYZING MEASURED RESPONSE(2010-07-14) Choi, MyoungA 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.