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Durham e-Theses
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Bayes Linear Variance Learning for Mixed Linear
Temporal Models

RANDELL, DAVID (2012) Bayes Linear Variance Learning for Mixed Linear
Temporal Models.
Doctoral thesis, Durham University.

PDF - Accepted Version


Modelling of complex corroding industrial systems is ritical to effective inspection and maintenance for ssurance of system integrity. Wall thickness and corrosion
rate are modelled for multiple dependent corroding omponents, given observations of minimum wall thickness per component. At each inspection, partial observations of the system are considered. A Bayes Linear approach is adopted simplifying parameter estimation and avoiding often unrealistic distributional assumptions. Key system variances are modelled, making exchangeability assumptions to facilitate analysis for sparse inspection time-series. A utility based criterion is used to assess quality of inspection design and aid decision making. The model is applied to inspection data from pipework networks on a full-scale offshore platform.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Keywords:Bayes Linear, Inspection, Corrosion, Exchangeability, Dynamic Linear Model, DLM, Variance learning
Faculty and Department:Faculty of Science > Mathematical Sciences, Department of
Thesis Date:2012
Copyright:Copyright of this thesis is held by the author
Deposited On:20 Jun 2012 15:06

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