Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency

Recent developments in prognostic models have led to increased use of prognostic information to improve the benefits of condition-based maintenance (CBM). Existing CBM decision-making methods are often limited by the assumption that prognostics information is available for all components and by the neglect of economic loss due to system degradation in assessing benefits. This paper aims to find optimal maintenance decisions with maximum net revenue for multi-component systems using prognostics information. In contrast to the reported works, three main contributions are made. First, considering that the remaining useful life (RUL) of only some components in the system can be predicted continuously, maintenance decisions, including planned inspection intervals and prognostic thresholds, are optimized jointly at the system-level. Second, the loss of efficiency due to system degradation is investigated when assessing the benefits of maintenance decisions. A degradation-related system efficiency model is proposed, and its parameters can be determined from historical data. Third, we extend our model with prognostic error modeling to derive insights on the effects of the prognostic model on the relative benefit of CBM. A case of maintenance optimization of wind turbine farms is provided to demonstrate and validate the proposed method.

Automated summary

This study aims to optimize maintenance decisions for multi-component systems using prognostics information, considering the continuous prediction of remaining useful life (RUL) for some components. It also investigates the impact of system degradation on the benefits of maintenance decisions and extends the model with prognostic error modeling. A case of wind turbine farms is used to demonstrate and validate the proposed method.