| Summary: | The degradation of aging energy infrastructure systems has the potential to increase the risk of failure, resulting in power outage and costly unplanned maintenance work. Therefore, the development of scientific and cost-effective life cycle management (LCM) strategies has become increasingly important to maintain energy infrastructure. Since degradation of aging equipment is an uncertain process which depends on many factors, a risk-based approach is required to consider the effect of various uncertainties in LCM.
The thesis presents probabilistic models to support risk-based life cycle management of energy infrastructure systems. In addition to uncertainty in degradation process, the inspection data collected by the energy industry is often censored and truncated which make it difficult to estimate the lifetime probability distribution of the equipment. The thesis presents modern statistical techniques in quantifying uncertainties associated with inspection data and to estimate the lifetime distributions in a consistent manner.
Age-based and sequential inspection-based replacement models are proposed for maintenance of component in a large-distribution network. A probabilistic lifetime model to consider the effect of imperfect preventive maintenance of a component is developed and its impact to maintenance optimization is illustrated.
The thesis presents a stochastic model for the pitting corrosion process in steam generators (SG), which is a serious form of degradation in SG tubing of some nuclear generating stations. The model is applied to estimate the number of tubes requiring plugging and the probability of tube leakage in an operating period. The application and benefits of the model are illustrated in the context of managing the life cycle of a steam generator.
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