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Reliability characteristics of new devices are usually demonstrated by life testing. When lifetime data are sparse, as is often the case with highly reliable devices, expensive devices, and devices for which accelerated life testing is not feasible, reliability models that are based on a combination of degradation and lifetime data represent an important practical approach. This thesis presents reliability models based on the combination of degradation and lifetime data or degradation data alone, with and without the presence of covariates. Statistical inference methods associated with the models are also developed. / The degradation process is assumed to follow a Wiener process. Failure is defined as the first passage of this process to a fixed barrier. The degradation data of a surviving item are described by a truncated Wiener process and lifetimes follow an inverse Gaussian distribution. Models are developed for three types of data structures that are often encountered in reliability studies, terminal point data (a combination of degradation and lifetime data) and mixed data (an extended case of terminal point data); conditional degradation data; and covariate data. / Maximum likelihood estimators (MLEs) are derived for the parameters of each model. Inferences about the parameters are based on asymptotic properties of the MLEs and on the likelihood ratio method. An analysis of deviance is presented and approximate pivotal quantities are derived for the drift and variance parameters. Predictive density functions for the lifetime and the future degradation level of either a surviving item or a new item are obtained using empirical Bayes methods. Case examples are given to illustrate the applications of the models.

Accelerated degradation testing is widely accepted in competitive industries. As there is no longer the need to test till failures, there are tremendous cost and time benefits on fully capitalizing on such a testing regime. Consequently, this research has aimed for better understanding of the relationship between design and degradation using the degradation data. Existing work in the literature uses the degradation data to improve the reliability of products. The majority of techniques, however, are centred on statistical experimental methods. For problems with increasing complexities such as large multivariable data set, non-linear interactions and dynamic varying processes, conventional methods cannot resolve the problem efficiently. Furthermore, it can not provide the adequate modelling mechanism to learn from the degradation data autonomously for describing the relationship between the design parameters and degradation. Artificial neural network is widely used for complex problems in the literature. This thesis proposes and demonstrates the neural network modelling methodology into capturing the non parametric relationship between design and degradation. / The development of a neural network consists of data preparation, network design and training and testing. This thesis presents a comprehensive description on the data generation and acquisition process. More specifically, the physical tests, experimental designs, equipment configurations, data acquisitions systems and algorithms are elaborated. Single hidden layer multilayered perceptrons are found to be the most suitable network architectures for the problem domains. Detailed descriptions of the training and testing process in determining the suitable number of hidden neurons sufficient for the problem are provided. / In summary, the neural network modelling methodology is demonstrated for the particular problem domain. As a result of the work in this thesis, two models of different practical significance are developed and compiled as Windows executables for predicting material performances. / Thesis (MEng(ManufacturingEngineering)--University of South Australia, 2006.

Thesis (MEng in Test and Evaluation)--University of South Australia, 1995

Thesis (M.S.)--Ohio University, August, 2003. / Title from PDF t.p. Includes bibliographical references (leaves 33-34).

Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Dr. Ume, I. Charles; Committee Member: Dr. Book, Wayne; Committee Member: Dr. Kim, Yeong; Committee Member: Dr. Pan, Jiahui; Committee Member: Dr. Sitaraman, Suresh; Committee Member: Dr. Wu, C. F. Jeff.

Thesis (M.S.)--Ohio University, March, 1995. / Title from PDF t.p.

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Schrage, Daniel; Committee Member: Loewy, Robert; Committee Member: O'Neill, Gary; Committee Member: Saleh, Joseph; Committee Member: Volovoi, Vitali. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 181-186). Also available via the Internet.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 33-38). Also available via the Internet.