Application of genetic algorithm to risk-based maintenance operations of liquefied natural gas carrier systems

被引:19
作者
Nwaoha, T. C. [1 ]
Yang, Z. [1 ]
Wang, J. [1 ]
Bonsall, S. [1 ]
机构
[1] Liverpool John Moores Univ, Sch Engn, Liverpool LOgist Offshore & Marine LOOM Ctr, Liverpool L3 3AF, Merseyside, England
关键词
liquefied natural gas carrier systems; risk; cost; genetic algorithm; maintenance; OPTIMIZATION;
D O I
10.1243/09544089JPME336
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The concept of genetic algorithm (GA) is used to model the cost of maintenance and repair of a liquefied natural gas (LNG) containment system and its transfer arm, after assessing the total risk of the systems using the probabilistic risk assessment technique. The failure frequency data of the basic events of the fault tree developed to model the LNG containment system and transfer arm, which is implemented and evaluated to estimate the failure frequencies of the systems, are derived from a careful literature search. A total risk formula is developed, which is dependent on hazard severity weight, failure frequencies, and the time and cost of maintenance and repair of the LNG carrier systems. The formula serves as the objective function, while new total cost allocated for the maintenance and repair of the LNG carrier systems as a whole is the constraint with the boundaries of presenting initial/unit cost of maintenance and repair of each containment system and transfer arm. Optimization is carried out on the objective function and its constraint for the identification of new cost of maintaining and repairing the containment system and transfer arm independently with the powerful tool of GA using Matlab version 7.7 software for improvement of the system's safety level.
引用
收藏
页码:40 / 52
页数:13
相关论文
共 43 条
  • [1] *ABS, 2000, GUID NOT RISK ASS AP
  • [2] New genetic algorithms (GA) to optimize PWR reactors - Part I: Loading pattern and burnable poison placement optimization techniques for PWRs
    Alim, Fatih
    Ivanov, Kostadin
    Levine, Samuel H.
    [J]. ANNALS OF NUCLEAR ENERGY, 2008, 35 (01) : 93 - 112
  • [3] A new approach to solve dynamic fault trees
    Amari, S
    Dill, G
    Howald, E
    [J]. ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM, 2003 PROCEEDINGS, 2003, : 374 - 379
  • [4] Genetic algorithm optimization of a firewater deluge system
    Andrews, JD
    Bartlett, LM
    [J]. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2003, 19 (01) : 39 - 52
  • [5] [Anonymous], CONS ASS METH INC IN
  • [6] [Anonymous], 1989, CHOICE REV ONLINE, DOI DOI 10.5860/CHOICE.27-0936
  • [7] [Anonymous], 1996, INTRO GENETIC ALGORI
  • [8] [Anonymous], 1999, Genetic Algorithms: Concepts and Designs
  • [9] [Anonymous], 2004, Wiley InterScience electronic collection.
  • [10] Choosing a heuristic for the "fault tree to binary decision diagram" conversion, using neural networks
    Bartlett, LM
    Andrews, JD
    [J]. IEEE TRANSACTIONS ON RELIABILITY, 2002, 51 (03) : 344 - 349