%0 Conference Proceedings
%T Integration of Decision Support Modules to Identify the Priority of Risk of Failure in Topside Piping Equipment: An Industrial Case Study from the NCS
%+ University of Stavanger
%A Seneviratne, A., B.
%A Ratnayake, R., Chandima
%Z Part 2: Case Studies
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B IFIP International Conference on Advances in Production Management Systems (APMS)
%C Ajaccio, France
%Y Bernard Grabot
%Y Bruno Vallespir
%Y Samuel Gomes
%Y Abdelaziz Bouras
%Y Dimitris Kiritsis
%I Springer
%3 Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World
%V AICT-440
%N Part III
%P 521-529
%8 2014-09-20
%D 2014
%R 10.1007/978-3-662-44733-8_65
%K In-service inspection planning
%K topside piping equipment
%K decision support modules
%K thickness measurement locations
%K artificial neural networks
%K ERP software
%Z Computer Science [cs]Conference papers
%X The identification and prioritization of locations that have potential for failure (also referred to as thickness measurement locations (TMLs)) in the in-service inspection planning of offshore topside piping equipment requires a significant amount of data analysis together with relevant information. In this context, planning personnel analyze data and information retrieved from piping inspection databases through enterprise resource planning (ERP) software to investigate possible degradation trends in order to recognize the TMLs that have reached a critical level. It is observed that suboptimal prioritization occurs due to time restriction vs. amount of data and/or information that has to be evaluated. The suboptimal prioritization omits some of the critical TMLs, increasing the risk of failures whilst also increasing cost due to taking non-critical TMLs into inspection. Therefore, this manuscript illustrates an approach to integrate the decision support modules (DSMs) via an artificial neural network model for the optimum prioritization.
%G English
%Z TC 5
%Z WG 5.7
%2 https://inria.hal.science/hal-01387316/document
%2 https://inria.hal.science/hal-01387316/file/978-3-662-44733-8_65_Chapter.pdf
%L hal-01387316
%U https://inria.hal.science/hal-01387316
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-APMS
%~ IFIP-WG5-7
%~ IFIP-AICT-440