Maintenance strategy optimization of pipeline system with multi-stage corrosion defects based on heuristically genetic algorithm

Abstract

To ensure the high reliability of the pipeline system, it is necessary to take group maintenance actions. This work proposes an optimal group maintenance approach based on a heuristically genetic algorithm to attain the system-performing goal while minimizing overall costs with assured system reliability. Multi-stage corrosion defects and multi-level risk areas are considered in this novel approach. The proposed method can sufficiently avoid pipeline failures by considering the multi-level probability of failure constraints. The methodology is illustrated by taking the 60-segment pipelines with multiple corrosion defects as an example. Numerical studies show that the proposed methodology can find an optimized maintenance strategy with a lower cost compared with the unified batch maintenance strategy. In addition, an optimal dynamic maintenance model is proposed and compared with the static model when the environment of the pipelines varies. Sensitivity analyses of the physical parameters of the phenomenon and the tuning parameters of the data-driven model show that the method has good effectiveness, compatibility, and universality.