Regression-based Statistical Change Point Analysis for Damage Localization

Structural health monitoring (SHM) research has become a vital tool in maintaining the integrity of structures that has been refined over the years. There are numerous methods for damage detection and localization; yet some are not efficient. For example, researchers have used dynamic properties as damage features to monitor a structure because they change in the presence of damage; however, these methods are global in nature. Research in improving them (i.e. having automated, statistical monitoring techniques) is critical to the advancement of the civil engineering field. This thesis presents the implementation of damage detection methods using an experimental structure. Damage features are created from linear regression models and are utilized in control charts to localize damage because they represent the changing properties of a structure in the event of damage. Therefore, this thesis evaluates the performance of different damage features and change point analysis methods in detecting and localizing damage

Sensor-based Nonlinear and Nonstationary Dynaimc Analysis of Online Structural Health Monitoring

This dissertation focuses on robust online Structural Health Monitoring (SHM) framework for civil engineering structures. The proposed framework improves the diagnostic and prognostic schemes for damage-state awareness and structural life prediction in civil engineering structures. The underlying research achieves three main objectives, namely, (1) sensor placement optimization using partial differential equation modeling and Fisher information matrix, (2) structural damage detection using quasi-recursive correlation dimension (QRCD), and (3) structural damage prediction using online empirical mode decomposition (EMD).The research methodology includes three research tasks: Firstly, to formulate the optimal criteria for the sensor placement optimization damage detection problem based upon a partial differential equation (PDE) analytical model. The PDE model is derived and then validated through experimental results using correlation analysis. Secondly, to develop a novel quasi-recursive correlation dimension method for structural damage detection. The QRCD algorithm is integrated with an attractor analysis and overlapping windowing technique. Thirdly, to design an online structural damage prediction method based on empirical mode decomposition. The proposed SHM prediction scheme consists of two steps: prediction based change point detection using Hilbert instantaneous phase, and damage severity prediction using the energy index of the most representative intrinsic mode function (IMF).Study results show that; (1) the proposed optimal sensor placement method leads to an optimal spatial location for a collection of sensors, which are sensitive to structural damage, (2) the proposed damage detection algorithm can significantly alleviate the complexity of computation for correlation dimension to approximate O(N), making the online monitoring of nonlinear/nonstationary processes more applicable and efficient; and (3) the proposed empirical mode decomposition method for online damage prediction overcomes the boundary effects of the sifting process, and it has significant prediction accuracy improvement (greater than 30%) over other commonly used prediction techniques.Industrial Engineering & Managemen

The management of burn wounds by nurses

A thesis submitted to the Faculty of Health Sciences, University of the Witwatersrand, in fulfilment of the requirements for the degree of Doctor of Philosophy Johannesburg, 2015A standardised approach to wound care is vital if a positive outcome is expected. The positive outcomes of standardisation and evidence based wound care protocols have been well documented, yet nurses in South Africa do not have a standard that informs burn wound management. The purpose of this study is to describe the best available evidence for management of burn wounds and to explore nurses’ current practices in a single burns unit with the aim of developing guidelines to inform nursing practices. A QUAN (quantitative dominant) QUAN+ QUAL (quantitative and quantitative concurrently), a non- experimental explanatory sequential descriptive design was used. The process was divided into three phases: Phase One involved the search for quality evidence through an integrative review. The main review question was: “What new knowledge or information related to non-surgical management of burn wounds has emerged in the literature between 2000 and 2014?” Eleven sub questions were used to guide the literature search according to the themes of the nursing process of: Assessment, Diagnosis, Intervention, Outcome and Evaluation. The review process included a problem identification stage, literature search stage, data evaluation stage, data analysis stage and presentation stage. The included literature was based on a hierarchy of evidence. The search strategy included: multiple electronic databases, hand searching, reference lists of relevant articles, comments of experts, textbook chapters compiled by experts and guidelines. The final sample consisted of n= 354 studies. A qualitative descriptive approach was used to synthesise the research findings. Phase Two involved the study of current practice through structured observation and semi-structured interviews. The purpose of Phase Two was to obtain first-hand information in a naturally occurring situation to identify the strengths, weaknesses and gaps in current practices. Purposive sampling was undertaken and included all nurses providing care to patients with superficial to partial thickness burn wounds. A total of n= 303 dressings were observed and eight interviews were conducted. Phase Three was the verification of findings from Phases One and Two by experts in the field using the AGREE II instrument. Conclusions drawn from observations and interviews were integrated and synthesised with the conclusions from the integrative review. These conclusions were used to develop guidelines for the management of burn wounds by nurses

Structural Health Monitoring and Application of Wireless Sensor Networks

Different elements of structural health monitoring (SHM) can benefit from the application of wireless sensor Networks (WSNs), as advanced sensing systems. While WSNs can significantly enhance the SHM by facilitating deployment of scalable and dense monitoring systems, challenges in the power consumption and data communication, and concerns regarding the possible impacts of their associated quality on the results have restricted their broad application. This research contributes in addressing the limitation associated with the prohibitive data communication delay and power consumption by introducing a novel time- and energy-efficient distributed algorithm for modal identification, and also addressing the concerns regarding the possible effects of their sensing quality by development of quality assessment approaches for modal identification and damage detection practices. The onboard processing techniques attempt to reduce the communication and power consumption by pushing the computation into the network. Efforts in developing onboard processing algorithms are restricted by the topology and algorithms, and their efficiency is not high enough to alleviate the challenge. A novel approach for modal identification of structural systems in a distributed scheme is developed which assigns the entire computational task of modal identification to remote nodes and limits the communication to transmission of only system\u27s parameters. The algorithm is based on estimation-updating steps at remote nodes and iterations by passing the results through the network for convergence of estimation. The algorithm is first developed for input-output scenarios and then is further expanded to address output-only systems as well. Development of approaches such as Cumulative System Formation for providing initial estimates of the system (as starting point of iteration) and also a novel AR-ARX approach for expediting the convergence also further enhanced the developed algorithm. Experiments and implementations have proved the functionality and performance of the algorithm. While the focus of the research is on development of algorithms for enhancing the application of wireless sensors in modal identification, other aspects of data-driven SHM such as damage detection, and performance evaluation through field-testing of real-life structures are also studied. A framework for damage detection algorithm including accuracy indicators and statistical approaches for change point detection is developed and validated through implementation on different experimental models. Moreover, the state of the art in structural monitoring and vibration evaluation is presented in two field deployments

Modelo de apoio à decisão para a manutenção condicionada de equipamentos produtivos

Doctoral Thesis for PhD degree in Industrial and Systems EngineeringIntroduction: This thesis describes a methodology to combine Bayesian control chart and CBM (Condition-Based Maintenance) for developing a new integrated model. In maintenance management, it is a challenging task for decision-maker to conduct an appropriate and accurate decision. Proper and well-performed CBM models are beneficial for maintenance decision making. The integration of Bayesian control chart and CBM is considered as an intelligent model and a suitable strategy for forecasting items failures as well as allow providing an effectiveness maintenance cost. CBM models provides lower inventory costs for spare parts, reduces unplanned outage, and minimize the risk of catastrophic failure, avoiding high penalties associated with losses of production or delays, increasing availability. However, CBM models need new aspects and the integration of new type of information in maintenance modeling that can improve the results. Objective: The thesis aims to develop a new methodology based on Bayesian control chart for predicting failures of item incorporating simultaneously two types of data: key quality control measurement and equipment condition parameters. In other words, the project research questions are directed to give the lower maintenance costs for real process control. Method: The mathematical approach carried out in this study for developing an optimal Condition Based Maintenance policy included the Weibull analysis for verifying the Markov property, Delay time concept used for deterioration modeling and PSO and Monte Carlo simulation. These models are used for finding the upper control limit and the interval monitoring that minimizes the (maintenance) cost function. Result: The main contribution of this thesis is that the proposed model performs better than previous models in which the hypothesis of using simultaneously data about condition equipment parameters and quality control measurements improve the effectiveness of integrated model Bayesian control chart for Condition Based Maintenance.Introdução: Esta tese descreve uma metodologia para combinar Bayesian control chart e CBM (Condition- Based Maintenance) para desenvolver um novo modelo integrado. Na gestão da manutenção, é importante que o decisor possa tomar decisões apropriadas e corretas. Modelos CBM bem concebidos serão muito benéficos nas tomadas de decisão sobre manutenção. A integração dos gráficos de controlo Bayesian e CBM é considerada um modelo inteligente e uma estratégica adequada para prever as falhas de componentes bem como produzir um controlo de custos de manutenção. Os modelos CBM conseguem definir custos de inventário mais baixos para as partes de substituição, reduzem interrupções não planeadas e minimizam o risco de falhas catastróficas, evitando elevadas penalizações associadas a perdas de produção ou atrasos, aumentando a disponibilidade. Contudo, os modelos CBM precisam de alterações e a integração de novos tipos de informação na modelação de manutenção que permitam melhorar os resultados.Objetivos: Esta tese pretende desenvolver uma nova metodologia baseada Bayesian control chart para prever as falhas de partes, incorporando dois tipos de dados: medições-chave de controlo de qualidade e parâmetros de condição do equipamento. Por outras palavras, as questões de investigação são direcionadas para diminuir custos de manutenção no processo de controlo.Métodos: Os modelos matemáticos implementados neste estudo para desenvolver uma política ótima de CBM incluíram a análise de Weibull para verificação da propriedade de Markov, conceito de atraso de tempo para a modelação da deterioração, PSO e simulação de Monte Carlo. Estes modelos são usados para encontrar o limite superior de controlo e o intervalo de monotorização para minimizar a função de custos de manutenção.Resultados: A principal contribuição desta tese é que o modelo proposto melhora os resultados dos modelos anteriores, baseando-se na hipótese de que, usando simultaneamente dados dos parâmetros dos equipamentos e medições de controlo de qualidade. Assim obtém-se uma melhoria a eficácia do modelo integrado de Bayesian control chart para a manutenção condicionada

Damage identification in bridge structures : review of available methods and case studies

Bridges are integral parts of the infrastructure and play a major role in civil engineering. Bridge health monitoring is necessary to extend the life of a bridge and retain safety. Periodic monitoring contributes significantly in keeping these structures operational and extends structural integrity. Different researchers have proposed different methods for identifying bridge damages based on different theories and laboratory tests. Several review papers have been published in the literature on the identification of damage and crack in bridge structures in the last few decades. In this paper, a review of literature on damage identification in bridge structures based on different methods and theories is carried out. The aim of this paper is to critically evaluate different methods that have been proposed to detect damages in different bridges. Different papers have been carefully reviewed, and the gaps, limitations, and superiority of the methods used are identified. Furthermore, in most of the reviews, future applications and several sustainable methods which are necessary for bridge monitoring are covered. This study significantly contributes to the literature by critically examining different methods, giving guidelines on the methods that identify the damages in bridge structures more accurately, and serving as a good reference for other researchers and future works

Six Sigma

In the new millennium the increasing expectation of customers and products complexity has forced companies to find new solutions and better alternatives to improve the quality of their products. Lean and Six Sigma methodology provides the best solutions to many problems and can be used as an accelerator in industry, business and even health care sectors. Due to its flexible nature, the Lean and Six Sigma methodology was rapidly adopted by many top and even small companies. This book provides the necessary guidance for selecting, performing and evaluating various procedures of Lean and Six Sigma. In the book you will find personal experiences in the field of Lean and Six Sigma projects in business, industry and health sectors

Physics-based and Data-driven Methods with Compact Computing Emphasis for Structural Health Monitoring

This doctoral dissertation contributes to both model-based and model-free data interpretation techniques in vibration-based Structural Health Monitoring (SHM). In the model-based category, a surrogate-based finite element (FE) model updating algorithm is developed to improve the computational efficiency by replacing the FE model with Response Surface (RS) polynomial models in the optimization problem of model calibration. In addition, formulation of the problem in an iterative format in time domain is proposed to extract more information from measured signals and compensate for the error present in the regressed RS models. This methodology is applied to a numerical case study of a steel frame with global nonlinearity. Its performance in presence of measurement noise is compared with a method based on sensitivity analysis and it is observed that while having comparable accuracy, proposed method outperforms the sensitivity-based model updating procedure in terms of required time. With the assumption of Gaussian measurement noise, it is also shown that this parameter estimation technique has low sensitivity to the standard deviation of the measurement noise. This is validated through several parametric sensitivity studies performed on numerical simulations of nonlinear systems with single and multiple degrees of freedom. The results show the least sensitivity to measurement noise level, selected time window for model updating, and location of the true model parameters in RS regression domain, when vibration frequency of the system is outside the frequency bandwidth of the load. Further application of this method is also presented through a case study of a steel frame with bilinear material model under seismic loading. The results indicate the robustness of this parameter estimation technique for different cases of input excitation, measurement noise level, and true model parametersIn the model-free category, this dissertation presents data-driven damage identification and localization methods based on two-sample control statistics as well as damage-sensitive features to be extracted from single- and multivariate regression models. For this purpose, sequential normalized likelihood ratio test and two-sample t-test are adopted to detect the change in two families of damage features based on the coefficients of four different linear regression models. The performance of combinations of these damage features, regression models and control statistics are compared through a scaled two-bay steel frame instrumented with a dense sensor network and excited by impact loading. It is shown that the presented methodologies are successful in detecting the timing and location of the structural damage, while having acceptable false detection quality. In addition, it is observed that incorporating multiple mathematical models, damage-sensitive features and change detection tests improve the overall performance of these model-free vibration-based structural damage detection procedures. In order to extend the scalability of the presented data-driven damage detection methods, a compressed sensing damage localization algorithm is also proposed. The objective is accurate damage localization in a structural component instrumented with a dense sensor network, by processing data only from a subset of sensors. In this method, first a set of sensors from the network are randomly sampled. Measurements from these sampled sensors are processed to extract damage sensitive features. These features undergo statistical change point analysis to establish a new boundary for a local search of damage location. As the local search proceeds, probability of the damage location is estimated through a Bayesian procedure with a bivariate Gaussian likelihood model. The decision boundary and the posterior probability of the damage location are updated as new sensors are added to processing subset and more information about location of damage becomes available. This procedure is continued until enough evidence is collected to infer about damage location. Performance of this method is evaluated using a FE model of a cracked gusset plate connection. Pre- and post-damage strain distributions in the plate are used for damage diagnosis.Lastly, through study of potential causes of damage to the Washington Monument during the 2011 Virginia earthquake, this dissertation demonstrates the role that SHM techniques plays in improving the credibility of damage assessment and fragility analysis of the constructed structures. An FE model of the Washington Monument is developed and updated based on the dynamic characteristics of the structure identified through ambient vibration measurement. The calibrated model is used to study the behavior of the Monument during 2011 Virginia earthquake. This FE model is then modified to limit the tensile capacity of the grout material and previously cracked sections to investigate the initiation and propagation of cracking in several futuristic earthquake scenarios. The nonlinear FE model is subjected to two ensembles of site-compatible ground motions representing different seismic hazard levels for the Washington Monument, and occurrence probability of several structural and non-structural damage states is investigated