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A Multi-Objective Genetic Algorithm for determining efficient Risk-Based Inspection programs

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  • Moura, Márcio das Chagas
  • Lins, Isis Didier
  • Droguett, Enrique López
  • Soares, Rodrigo Ferreira
  • Pascual, Rodrigo

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.

Suggested Citation

  • Moura, Márcio das Chagas & Lins, Isis Didier & Droguett, Enrique López & Soares, Rodrigo Ferreira & Pascual, Rodrigo, 2015. "A Multi-Objective Genetic Algorithm for determining efficient Risk-Based Inspection programs," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 253-265.
    • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:253-265
    DOI: 10.1016/j.ress.2014.09.018
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    References listed on IDEAS

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    1. Bragatto, Paolo A. & Pittiglio, Paolo & Ansaldi, Silvia, 2009. "The management of mechanical integrity inspections at small-sized “Seveso†facilities," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 412-417.
    2. Chien, Chi-Hui & Chen, Chun-Hung & Chao, Yuh J., 2009. "A strategy for the risk-based inspection of pressure safety valves," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 810-818.
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    Cited by:

    1. Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
    2. Rachman, Andika & Ratnayake, R.M. Chandima, 2019. "Machine learning approach for risk-based inspection screening assessment," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 518-532.
    3. Syan, Chanan S. & Ramsoobag, Geeta, 2019. "Maintenance applications of multi-criteria optimization: A review," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    4. Hajipour, Yassin & Taghipour, Sharareh, 2016. "Non-periodic inspection optimization of multi-component and k-out-of-m systems," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 228-243.
    5. Pombo, A. Vieira & Murta-Pina, João & Pires, V. Fernão, 2015. "Multiobjective planning of distribution networks incorporating switches and protective devices using a memetic optimization," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 101-108.

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