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Fault detection and diagnosis of gear transmission systems have attracted considerable attention in recent years, due to the need to decrease the downtime on production machinery and to reduce the extent of the secondary damage caused by failures. However, little research has been done to develop gear shaft and planetary gear crack detection methods based on vibration signal analysis. In this thesis, an approach to gear shaft and planetary gear fault detection based on the application of the wavelet transform to both the time synchronously averaged (TSA) signal and residual signal is presented. Wavelet approaches themselves are sometimes inefficient for picking up the fault signal characteristic under the presence of strong noise. In this thesis, the autocovariance of maximal energy wavelet coefficients is first proposed to evaluate the gear shaft and planetary gear fault advancement quantitatively. For a comparison, the advantages and disadvantages of some approaches such as using variance, kurtosis, the application of the Kolmogorov-Smirnov test (K-S test), root mean square (RMS) , and crest factor as fault indicators with continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for residual signal, are discussed. It is demonstrated using real vibration data that the early faults in gear shafts and planetary gear can be detected and identified successfully using wavelet transforms combined with the approaches mentioned above. In the second part of the thesis, the planetary gear deterioration process from the new condition to failure is modeled as a continuous time homogeneous Markov process with three states: good, warning, and breakdown. The observation process is represented by two characteristics: variance and RMS based on the analysis of autocovariance of DWT applied to the TSA signal obtained from planetary gear vibration data. The hidden Markov model parameters are estimated by maximizing the pseudo likelihood function using the EM iterative algorithm. Then, a multivariate Bayesian control chart is applied for fault detection. It can be seen from the numerical results that the Bayesian chart performs better than the traditional Chi-square chart.

Fault detection and diagnosis of gear transmission systems have attracted considerable attention in recent years, due to the need to decrease the downtime on production machinery and to reduce the extent of the secondary damage caused by failures. However, little research has been done to develop gear shaft and planetary gear crack detection methods based on vibration signal analysis. In this thesis, an approach to gear shaft and planetary gear fault detection based on the application of the wavelet transform to both the time synchronously averaged (TSA) signal and residual signal is presented. Wavelet approaches themselves are sometimes inefficient for picking up the fault signal characteristic under the presence of strong noise. In this thesis, the autocovariance of maximal energy wavelet coefficients is first proposed to evaluate the gear shaft and planetary gear fault advancement quantitatively. For a comparison, the advantages and disadvantages of some approaches such as using variance, kurtosis, the application of the Kolmogorov-Smirnov test (K-S test), root mean square (RMS) , and crest factor as fault indicators with continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for residual signal, are discussed. It is demonstrated using real vibration data that the early faults in gear shafts and planetary gear can be detected and identified successfully using wavelet transforms combined with the approaches mentioned above. In the second part of the thesis, the planetary gear deterioration process from the new condition to failure is modeled as a continuous time homogeneous Markov process with three states: good, warning, and breakdown. The observation process is represented by two characteristics: variance and RMS based on the analysis of autocovariance of DWT applied to the TSA signal obtained from planetary gear vibration data. The hidden Markov model parameters are estimated by maximizing the pseudo likelihood function using the EM iterative algorithm. Then, a multivariate Bayesian control chart is applied for fault detection. It can be seen from the numerical results that the Bayesian chart performs better than the traditional Chi-square chart.

In this dissertation we present mathematical models that help answer health policy questions relating to HIV and Hepatitis C (HCV), and analyze bias in Markov models of disease progression. We begin by developing a Markov decision process model that examines the timing of testing and treatment for diseases with asymptomatic periods such as HCV. We explicitly consider secondary infections, false positives and negatives, and behavioral modification from information from test results. We derive sufficient conditions for testing and/or treating in a dynamic environment, i.e., when unscheduled patients arrive. We also develop a detailed simulation model for general testing and/or treating for HCV. A key finding is that the current policy recommendations on testing for HCV may be too restrictive, and that it is cost-effective to test the overall population if done at the appropriate times. The Markov models used in the study of HCV motivated the next topic where we examine bias in Markov models of diseases. We examine models in which the progression of the disease varies with severity and find sufficient conditions for bias to exist in models that do not allow for transition probabilities to change with disease severity. We apply the results to HCV and find that the bias is significant depending on the method used to aggregate the disease data. We close with a discussion on a specific question in HIV policy where we develop a Bernoulli process transmission model in which, for a given individual, each risky person-to-person contact is treated as an independent Bernoulli trial. Using the model and data from the Urban Men's Health Study, we estimate the affect that interventions at venues, namely bathhouses, in which high-risk behavior takes place would have on HIV transmission.

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Made available in DSpace on 2014-12-17T14:55:43Z (GMT). No. of bitstreams: 1 RicardoBM_DISSERT.pdf: 1094194 bytes, checksum: b8e5943b9e567c5466093b97b36b90c2 (MD5) Previous issue date: 2009-12-09 / This study aims to use a computational model that considers the statistical characteristics of the wind and the reliability characteristics of a wind turbine, such as failure rates and repair, representing the wind farm by a Markov process to determine the estimated annual energy generated, and compare it with a real case. This model can also be used in reliability studies, and provides some performance indicators that will help in analyzing the feasibility of setting up a wind farm, once the power curve is known and the availability of wind speed measurements. To validate this model, simulations were done using the database of the wind farm of Macau PETROBRAS. The results were very close to the real, thereby confirming that the model successfully reproduced the behavior of all components involved. Finally, a comparison was made of the results presented by this model, with the result of estimated annual energy considering the modeling of the distribution wind by a statistical distribution of Weibull / Este trabalho tem por objetivo, utilizar um modelo computacional que considera as caracter?sticas estat?sticas do vento e as caracter?sticas de confiabilidade de uma turbina e?lica, tais como taxas de falha e de reparo, representando a usina e?lica por um processo de Markov, para determina??o da estimativa anual da energia gerada e compar?-la com um caso real. Este modelo tamb?m pode ser utilizado em estudos de confiabilidade, al?m de fornecer alguns indicadores de desempenho, que ajudar?o na an?lise de viabilidade de implanta??o de uma usina e?lica, uma vez conhecida a curva de pot?ncia do aerogerador e dispondo-se de medi??es anemom?tricas da velocidade do vento. Para a valida??o deste modelo, foram feitas simula??es utilizando o banco de dados da usina e?lica de Macau da PETROBRAS. Os resultados obtidos foram bem pr?ximos do real, confirmando, assim, que o modelo reproduziu com sucesso o comportamento de todos os componentes envolvidos. Finalmente, foi feita uma compara??o dos resultados apresentados por este modelo, com o resultado da energia anual estimada considerando a modelagem do comportamento do vento por uma distribui??o estat?stica de Weibull

Background: Diabetes is a common and costly disease that is expected to continue even to grow in prevalence and health expenditures over the coming decades. Type 2 diabetes is the most common diabetes type and is characterized by insulin resistance and relative insulin deficiency. Type 2 diabetes develops over a long period and is often undetected over years. During this time, people almost always first develop any of the pre-diabetic states, i.e. impaired fasting glucose (IFG), impaired glucose tolerance (IGT) or a combination of both (IFG&IGT). This thesis focuses on type 2 diabetes only. In the following, the term diabetes is used to refer to type 2 diabetes only. Diabetes is associated with a sedentary lifestyle and obesity. While those are not the only factors contributing to the development and maintenance of diabetes, several studies have shown that prevention of diabetes among individuals at high risk through lifestyle change is possible, effective and cost-effective, especially targeting diet and exercise to reduce weight. No previous study had, however, estimated the cost-effectiveness of diabetes prevention strategies from a population-based perspective including healthy individuals and also considered IFG and IGT as two distinct pre-diabetic states. Objective: The overall objective of this thesis was to establish, describe and evaluate a model that can assess the cost-effectiveness of lifestyle intervention programs to prevent diabetes. Methods: First, a Markov Model was established using data from the literature. The cost of a German diabetes prevention program was estimated. Second, risk equations for change to worsened glucose states were estimated using factor analysis and logistic regression based on consecutive data from the Västerbotten Intervention Program (VIP). The risk equations described transition probabilities in the final model and were based on several risk factors such as age, sex, physical activity and smoking status. Third, information on the Short-Form 36 questionnaire from the VIP population was transformed into Short-Form 6D. Health utility weights (HUW) by glucose group and four risk factors were estimated using beta regression. Fourth, an updated Markov model was established using an updated model structure compared to the one in Paper I, program costs of Paper I, risk equations of Paper II, health utility weights of Paper III and updated cost and mortality estimates. Results: The first model in Paper I showed that lifestyle intervention programs have the potential to be cost-effective with a high degree of uncertainty. The risk equations in Paper II indicated that the impact of each risk factor depended on the starting and ending pre-diabetes state, where high levels of triglyceride, hypertension, and high body mass index were the strongest risk factors to transit to a worsened glucose state. The overall mean HUW in Paper III was 0.764 with healthy individuals having the highest HUW, those with diabetes the lowest and those in pre-diabetic states ranging in between. The intervention described in Paper IV was cost-effective for all sex and age scenarios ranging from 3,833 EUR/QALY gained (women, 30 years) to 9,215 EUR/QALY gained (men, 70 years). The probability that the intervention is cost-effective was high (85.0-91.1%). Conclusion: We established a model that can estimate the cost-effectiveness of different scenarios of initiatives to prevent diabetes. The prevention or the delay of the onset of diabetes is feasible and cost-effective. A small investment in a healthy lifestyle with the change in physical activity and diet together with weight loss can have a decent, cost-effective result. The full range of possibilities this model offers has not been evaluated so far. We have, however, shown that implementing a lifestyle intervention program like the Västerbotten Intervention Programme would be cost-effective.

A presente dissertação apresenta e propõe um modelo para o monitoramento e avaliação de um sistema de distribuição de energia utilizando a técnica de manutenção com base nas condições de uso aplicada aos transformadores de potência e distribuição. O monitoramento dos transformadores baseado nas suas condições de uso permite inferir a taxa de degradação deste equipamento, de modo que a sua manutenção seja realizada de forma preventiva e não corretiva. A eficiência deste método de monitoramento é analisada em um sistema de distribuição de energia elétrica por meio do emprego do modelo de Markov. / This work presents and proposes a model to supervise and evaluate an electrical energy distribution system by applying the usage conditions based maintenance technique on the power and distribution transformers. Monitoring the distribution system transformers with the usage conditions technique allows the system administrator to perform a preventive maintenance instead of a corrective maintenance. The efficiency of this technique is evaluated on an electrical energy distribution system through the employment of the Markov model.

A presente dissertação apresenta e propõe um modelo para o monitoramento e avaliação de um sistema de distribuição de energia utilizando a técnica de manutenção com base nas condições de uso aplicada aos transformadores de potência e distribuição. O monitoramento dos transformadores baseado nas suas condições de uso permite inferir a taxa de degradação deste equipamento, de modo que a sua manutenção seja realizada de forma preventiva e não corretiva. A eficiência deste método de monitoramento é analisada em um sistema de distribuição de energia elétrica por meio do emprego do modelo de Markov. / This work presents and proposes a model to supervise and evaluate an electrical energy distribution system by applying the usage conditions based maintenance technique on the power and distribution transformers. Monitoring the distribution system transformers with the usage conditions technique allows the system administrator to perform a preventive maintenance instead of a corrective maintenance. The efficiency of this technique is evaluated on an electrical energy distribution system through the employment of the Markov model.

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