Home > Research > Publications & Outputs > Bayesian linear inspection planning for large-s...

Research

Links

Text available via DOI:

Keywords

View graph of relations

Bayesian linear inspection planning for large-scale physical systems

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Bayesian linear inspection planning for large-scale physical systems. / Randell, D.; Goldstein, M.; Hardman, G. et al.
In: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, Vol. 224, No. 4, 2010, p. 333-345.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Randell, D, Goldstein, M, Hardman, G, Jonathan, P & Troffaes, M 2010, 'Bayesian linear inspection planning for large-scale physical systems', Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 224, no. 4, pp. 333-345. https://doi.org/10.1243/1748006XJRR322

APA

Randell, D., Goldstein, M., Hardman, G., Jonathan, P., & Troffaes, M. (2010). Bayesian linear inspection planning for large-scale physical systems. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 224(4), 333-345. https://doi.org/10.1243/1748006XJRR322

Vancouver

Randell D, Goldstein M, Hardman G, Jonathan P, Troffaes M. Bayesian linear inspection planning for large-scale physical systems. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2010;224(4):333-345. doi: 10.1243/1748006XJRR322

Author

Randell, D. ; Goldstein, M. ; Hardman, G. et al. / Bayesian linear inspection planning for large-scale physical systems. In: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2010 ; Vol. 224, No. 4. pp. 333-345.

Bibtex

@article{470945c3c40849f8bd53573ffef31c41,
title = "Bayesian linear inspection planning for large-scale physical systems",
abstract = "Modelling of complex corroding industrial systems is critical to effective inspection and maintenance for assurance of system integrity. Wall thickness and corrosion rate are modelled for multiple dependent corroding components, 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.",
keywords = "Bayes linear, Corrosion, Dynamic linear model, Exchangeability, Inspection planning, Variance learning, Corrosion rate, Decision making, Drilling platforms, Offshore structures, Parameter estimation, Time series, Time series analysis, Walls (structural partitions), Inspection",
author = "D. Randell and M. Goldstein and G. Hardman and P. Jonathan and M. Troffaes",
year = "2010",
doi = "10.1243/1748006XJRR322",
language = "English",
volume = "224",
pages = "333--345",
journal = "Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability",
issn = "1748-006X",
publisher = "SAGE Publications Ltd",
number = "4",

}

RIS

TY - JOUR

T1 - Bayesian linear inspection planning for large-scale physical systems

AU - Randell, D.

AU - Goldstein, M.

AU - Hardman, G.

AU - Jonathan, P.

AU - Troffaes, M.

PY - 2010

Y1 - 2010

N2 - Modelling of complex corroding industrial systems is critical to effective inspection and maintenance for assurance of system integrity. Wall thickness and corrosion rate are modelled for multiple dependent corroding components, 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.

AB - Modelling of complex corroding industrial systems is critical to effective inspection and maintenance for assurance of system integrity. Wall thickness and corrosion rate are modelled for multiple dependent corroding components, 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.

KW - Bayes linear

KW - Corrosion

KW - Dynamic linear model

KW - Exchangeability

KW - Inspection planning

KW - Variance learning

KW - Corrosion rate

KW - Decision making

KW - Drilling platforms

KW - Offshore structures

KW - Parameter estimation

KW - Time series

KW - Time series analysis

KW - Walls (structural partitions)

KW - Inspection

U2 - 10.1243/1748006XJRR322

DO - 10.1243/1748006XJRR322

M3 - Journal article

VL - 224

SP - 333

EP - 345

JO - Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

JF - Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

SN - 1748-006X

IS - 4

ER -