Efficient Bayesian sampling inspection for industrial processes based on transformed spatio-temporal data

Mathematics and Statistics

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https://doi.org/10.1191/1471082X04st081oa
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Keywords

Bayesian, corrosion, decision support, DLM, industrial statistics, inspection, minima, optimal experimental design, spatio-temporal

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Efficient Bayesian sampling inspection for industrial processes based on transformed spatio-temporal data. / Little, J.; Goldstein, M.; Jonathan, P.
In: Statistical Modelling, Vol. 4, No. 4, 2004, p. 299-313.

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

Bibtex

@article{1a18fdb8428246e6a6ef8fad179bf927,

title = "Efficient Bayesian sampling inspection for industrial processes based on transformed spatio-temporal data",

abstract = "Efficient inspection and maintenance of complex industrial systems, subject to degradation effects such as corrosion, are important for safety and economic reasons. With appropriate statistical modelling, the utilization of inspection resources and the quality of inferences can be greatly improved. We develop a suitable Bayesian spatio-temporal dynamic linear model for problems such as wall thickness monitoring. We are concerned with problems where the inspection method used collects transformed data, for example minimum regional remaining wall thicknesses. We describe how the model may be used to derive efficient inspection schedules by identifying when, where and how much inspection should be made in the future. {\textcopyright} 2004, Sage Publications. All rights reserved.",

keywords = "Bayesian, corrosion, decision support, DLM, industrial statistics, inspection, minima, optimal experimental design, spatio-temporal",

author = "J. Little and M. Goldstein and P. Jonathan",

year = "2004",

doi = "10.1191/1471082X04st081oa",

language = "English",

volume = "4",

pages = "299--313",

journal = "Statistical Modelling",

issn = "1471-082X",

publisher = "SAGE Publications Ltd",

number = "4",

}

RIS

TY - JOUR

T1 - Efficient Bayesian sampling inspection for industrial processes based on transformed spatio-temporal data

AU - Little, J.

AU - Goldstein, M.

AU - Jonathan, P.

PY - 2004

Y1 - 2004

N2 - Efficient inspection and maintenance of complex industrial systems, subject to degradation effects such as corrosion, are important for safety and economic reasons. With appropriate statistical modelling, the utilization of inspection resources and the quality of inferences can be greatly improved. We develop a suitable Bayesian spatio-temporal dynamic linear model for problems such as wall thickness monitoring. We are concerned with problems where the inspection method used collects transformed data, for example minimum regional remaining wall thicknesses. We describe how the model may be used to derive efficient inspection schedules by identifying when, where and how much inspection should be made in the future. © 2004, Sage Publications. All rights reserved.

AB - Efficient inspection and maintenance of complex industrial systems, subject to degradation effects such as corrosion, are important for safety and economic reasons. With appropriate statistical modelling, the utilization of inspection resources and the quality of inferences can be greatly improved. We develop a suitable Bayesian spatio-temporal dynamic linear model for problems such as wall thickness monitoring. We are concerned with problems where the inspection method used collects transformed data, for example minimum regional remaining wall thicknesses. We describe how the model may be used to derive efficient inspection schedules by identifying when, where and how much inspection should be made in the future. © 2004, Sage Publications. All rights reserved.

KW - Bayesian

KW - corrosion

KW - decision support

KW - DLM

KW - industrial statistics

KW - inspection

KW - minima

KW - optimal experimental design

KW - spatio-temporal

U2 - 10.1191/1471082X04st081oa

DO - 10.1191/1471082X04st081oa

M3 - Journal article

VL - 4

SP - 299

EP - 313

JO - Statistical Modelling

JF - Statistical Modelling

SN - 1471-082X

IS - 4

ER -

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