A Multi-Objective Genetic Algorithm for determining efficient Risk-Based Inspection programs. (January 2015)
- Record Type:
- Journal Article
- Title:
- A Multi-Objective Genetic Algorithm for determining efficient Risk-Based Inspection programs. (January 2015)
- Main Title:
- A Multi-Objective Genetic Algorithm for determining efficient Risk-Based Inspection programs
- Authors:
- Moura, Márcio das Chagas
Lins, Isis Didier
Droguett, Enrique López
Soares, Rodrigo Ferreira
Pascual, Rodrigo - Abstract:
- Abstract: This paper proposes a coupling between Risk-Based Inspection (RBI) methodology and Multi-Objective Genetic Algorithm (MOGA) for defining efficient inspection programs in terms of inspection costs and risk level, which also comply with restrictions imposed by international standards and/or local government regulations. The proposed RBI+MOGA approach has the following advantages: (i) a user-defined risk target is not required; (ii) it is not necessary to estimate the consequences of failures; (iii) the inspection expenditures become more manageable, which allows assessing the impact of prevention investments on the risk level; (iv) the proposed framework directly provides, as part of the solution, the information on how the inspection budget should be efficiently spent. Then, genetic operators are tailored for solving this problem given the huge size of the search space. The ability of the proposed RBI+MOGA in providing efficient solutions is evaluated by means of two examples, one of them involving an oil and gas separator vessel subject to internal and external corrosion that cause thinning. The obtained results indicate that the proposed genetic operators significantly reduce the search space to be explored and RBI+MOGA is a valuable method to support decisions concerning the mechanical integrity of plant equipment. Highlights: This paper proposes an original RBI multi-objective-based framework. The exhaustive evaluation of these feasible programs is impossible inAbstract: This paper proposes a coupling between Risk-Based Inspection (RBI) methodology and Multi-Objective Genetic Algorithm (MOGA) for defining efficient inspection programs in terms of inspection costs and risk level, which also comply with restrictions imposed by international standards and/or local government regulations. The proposed RBI+MOGA approach has the following advantages: (i) a user-defined risk target is not required; (ii) it is not necessary to estimate the consequences of failures; (iii) the inspection expenditures become more manageable, which allows assessing the impact of prevention investments on the risk level; (iv) the proposed framework directly provides, as part of the solution, the information on how the inspection budget should be efficiently spent. Then, genetic operators are tailored for solving this problem given the huge size of the search space. The ability of the proposed RBI+MOGA in providing efficient solutions is evaluated by means of two examples, one of them involving an oil and gas separator vessel subject to internal and external corrosion that cause thinning. The obtained results indicate that the proposed genetic operators significantly reduce the search space to be explored and RBI+MOGA is a valuable method to support decisions concerning the mechanical integrity of plant equipment. Highlights: This paper proposes an original RBI multi-objective-based framework. The exhaustive evaluation of these feasible programs is impossible in practice. Thus, the effort to accomplish the analysis is fairly reduced. Tool to support efficient decisions related to mechanical integrity of equipment. … (more)
- Is Part Of:
- Reliability engineering & system safety. Volume 133(2015:Jan.)
- Journal:
- Reliability engineering & system safety
- Issue:
- Volume 133(2015:Jan.)
- Issue Display:
- Volume 133 (2015)
- Year:
- 2015
- Volume:
- 133
- Issue Sort Value:
- 2015-0133-0000-0000
- Page Start:
- 253
- Page End:
- 265
- Publication Date:
- 2015-01
- Subjects:
- Inspection programs -- Risk reduction -- Risk-Based Inspection -- Multi-Objective Genetic Algorithm
Reliability (Engineering) -- Periodicals
System safety -- Periodicals
Industrial safety -- Periodicals
Fiabilité -- Périodiques
Sécurité des systèmes -- Périodiques
Sécurité du travail -- Périodiques
620.00452 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09518320 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ress.2014.09.018 ↗
- Languages:
- English
- ISSNs:
- 0951-8320
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7356.422700
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 6099.xml