An efficient computational strategy for robust maintenance scheduling: application to corroded pipelines

The ability to predict correctly the future remaining life time of components is of paramount importance to improve the safety and reliability of systems and networks via an effective maintenance policy. However, simplifications and assumptions are usually adopted to compensate lack of data, imprecision and vagueness, which cannot be justified completely and may, thus lead to biased results. To overcome these issues, an imprecise probabilities approach is proposed for reliability analysis and risk-based maintenance strategy. A novel efficient computational approach is proposed for identifying robust maintenance strategies. The optimal solution is obtained through only one reliability assessment based on Advanced Line Sampling and reusing the outcome of maintenance activities in a force Monte Carlo approach. The proposed methodology remove the huge computational cost of reliability-base optimization making the analysis of industrial size problem feasible. The applicability of the approach is demonstrated by identifying the optimal maintenance policy of buried pipelines and it is shown how this approach can improve the current industrial practice.