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Author: Limon, Shah Mohammad

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    Reliability Estimation Considering Customer Usage Rate Profile and Warranty Claims
    (North Dakota State University, 2014) Limon, Shah Mohammad
    Providing more realistic reliability prediction based on small proportion of failed population or test data has always been a challenging task. Manufacturers rely heavily on reliability prediction for designing warranty plan. Further, to predict warranty claims for the remaining warranty period, it is important to have more realistic reliability assessment by considering a larger proportion of the population or the maximum possible information on the remaining population. However, generally this information is not readily available and is very difficult to gather on the scattered population. In this work, we propose to use customer usage rate profile to generate censored usage data on the remaining population that do not have any failure and warranty claim yet. We intend to use field data available such as warranty claims, field failures, recall data, and maintenance data to develop usage rate profile and subsequently estimate censored usage time. Finally, reliability estimation methodology is developed considering both censored data and field failure data to provide more reasonable reliability prediction for the remaining warranty period. The proposed methodology is demonstrated considering real life data from a big manufacturing company.
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    Assessing Reliability of Highly Reliable Products Using Accelerated Degradation Test Design, Modeling, and Bayesian Inference
    (North Dakota State University, 2019) Limon, Shah Mohammad
    The accelerated degradation test methods have proven to be a very effective approach to quickly evaluate the reliability of highly reliable products. However, the modeling of accelerated degradation test data to estimate reliability at normal operating condition is still a challenging task especially in the presence of multi-stress factors. In this study, a nonstationary gamma process is considered to model the degradation behavior assuming the strict monotonicity and non-negative nature of the product deterioration. It further assumes that both the gamma process parameters are stress dependent. A maximum likelihood method has been used for the model parameter estimation. The case study results indicate that traditional models that assume only shape parameter as stress dependent underestimate the product reliability significantly at normal operating conditions. This study further revealed that the scale parameter at a higher stress level is very close to the traditional constant assumption. However, at the normal operating condition, scale parameter value differs significantly with the traditional constant assumption value. This difference leads to the larger difference of reliability and lifetime estimates provided by the proposed approach. A Monte Carlo simulation with the Bayesian updating method has been incorporated to update the gamma parameters and reliability estimates when additional degradation data become available. A generalized reliability estimation framework for using the ADT data is also presented in this work. Further, in this work, an optimal constant-stress accelerated degradation test plan is presented considering the gamma process. The optimization criteria are set by minimizing the asymptotic variance of the maximum likelihood estimator of the lifetime at operating condition under total experimental cost constraint. A heuristic based more specifically genetic algorithm approach has been implemented to solve the model. Additionally, a sensitivity analysis is performed which revealed that increasing budget causes longer test duration time with smaller sample size. Also, it reduces the asymptotic variance of the estimation which is very intuitive as more budget increase the possibility to generate more degradation information and helps to increase the estimation accuracy. The overall reliability assessment methodology and the test design has been demonstrated using the carbon-film resistor degradation data.