In practical engineering, effects of random external shocks on the engineering system are unavoidable. The resistant ability of an engineering system can be improved by taking CBM operation. Meanwhile, further deterioration of system performance can be avoided with adopting emergency maintenance (EM) operation. A joint multi-objective optimization method for EM and CBM throughout the service life cycle is proposed in this chapter. A cumulative degradation prediction model is established with considering the competitive failure mechanism. In the multi-objective optimization model, the objectives are the performance loss ratio and maintenance cost in the overall service life cycle, and the priority of optimization objectives are determined according to the practical application. The joint maintenance optimization process is realized using the multi-objective particle swarm optimization algorithm to obtain the optimal maintenance thresholds of EM and CBM. Based on the optimal maintenance thresholds, reasonable maintenance management plan, such as maintenance sequence, can be further formulated.

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Joint Optimization for Emergency Maintenance and CBM

  • Baoping Cai,
  • Yiliu Liu,
  • Yonghong Liu,
  • Yixin Zhao,
  • Xiaoyan Shao

摘要

In practical engineering, effects of random external shocks on the engineering system are unavoidable. The resistant ability of an engineering system can be improved by taking CBM operation. Meanwhile, further deterioration of system performance can be avoided with adopting emergency maintenance (EM) operation. A joint multi-objective optimization method for EM and CBM throughout the service life cycle is proposed in this chapter. A cumulative degradation prediction model is established with considering the competitive failure mechanism. In the multi-objective optimization model, the objectives are the performance loss ratio and maintenance cost in the overall service life cycle, and the priority of optimization objectives are determined according to the practical application. The joint maintenance optimization process is realized using the multi-objective particle swarm optimization algorithm to obtain the optimal maintenance thresholds of EM and CBM. Based on the optimal maintenance thresholds, reasonable maintenance management plan, such as maintenance sequence, can be further formulated.