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Development of FIS for Identification of Potential Failure Locations in Topside Piping Sub-Systems: An Expert System Based Approach

[+] Author Affiliations
A. M. N. D. B. Seneviratne, R. M. Chandima Ratnayake

University of Stavanger, Stavanger, Norway

Paper No. OMAE2015-41519, pp. V001T01A049; 11 pages
doi:10.1115/OMAE2015-41519
From:
  • ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 1: Offshore Technology; Offshore Geotechnics
  • St. John’s, Newfoundland, Canada, May 31–June 5, 2015
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-5647-5
  • Copyright © 2015 by ASME

abstract

The identification of potential failure locations in ageing topside piping equipment of offshore production and process facilities (P&PFs) requires rigorous analyses. The risk based inspection (RBI) analysis methods defined in industrial standards classify the equipment according to the risk levels. The RBI analysis carried out on the plant equipment of P&PFs identifies the systems and sub-systems according to the different risk levels. However, the sub-systems (i.e. corrosion loops) are mainly defined according to the degradation behavior of the equipment. The equipment hierarchy for P&PFs is designed based on the RBI analysis outcome.

The major part of risk level evaluation is based on equipment’s probability of failure, which is related to degradation. The risk level analysis of equipment and thickness measurement locations (TMLs) within a sub-system is based on the expert engineering judgments of field experts. This analysis varies depending on the expertise of the evaluator in identifying and classifying critical TMLs in a sub-system. This manuscript illustrates an expert system using fuzzy inference systems (FISs) to identify the potential failure of TMLs in a sub-system suppressing the variation of analysis results given by different field experts. The method is illustrated by considering two degradation mechanisms (corrosion and erosion) to identify the probability of potential failure and integrating the production and process data to the FIS based expert system to generate a dynamic output.

Copyright © 2015 by ASME

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