Bayes linear variance structure learning for inspection of large scale physical systems

Randell, D. and Goldstein, M. and Jonathan, P. (2014) Bayes linear variance structure learning for inspection of large scale physical systems. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 228 (1). pp. 3-18. ISSN 1748-006X

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Abstract

Modelling of inspection data for large scale physical systems is critical to assessment of their integrity. We present a general method for inference about system state and associated model variance structure from spatially distributed time series that are typically short, irregular, incomplete and not directly observable. Bayes linear analysis simplifies parameter estimation and avoids often-unrealistic distributional assumptions. Second-order exchangeability judgements facilitate variance learning for sparse inspection time-series. The model is applied to inspection data for minimum wall thickness from corroding pipe-work networks on a full-scale offshore platform, and shown to give materially different forecasts of remnant life compared with an equivalent model neglecting variance learning. © 2013 IMechE.

Item Type:
Journal Article
Journal or Publication Title:
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2213
Subjects:
ID Code:
133055
Deposited By:
Deposited On:
22 Apr 2019 14:45
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Nov 2020 09:04