A HIERARCHICAL BAYESIAN NETWORK TO COMPARE MAINTENANCE STRATEGIES BASED ON COST AND RELIABILITY: A CASE OF ONSHORE WIND TURBINES

Today we encounter systems, which consist of several vital components interacting with environment, and organizational factors. This necessitates an approach which is enabled to consider various aspects of systems and underlying interactions. To clearly illustrate this concept, we develop a Bayesian network (BN). The model enables decision makers to trace the impacts of applying different maintenance strategies on subsystems reliabilities. The model is applied to evaluate various maintenance strategies impacts on the reliability of a wind turbine. A low reliable wind turbine suffers from high turbine failure rate leading to a high Cost of Energy (CoE) due to high Operating and Maintenance (O&M) costs, as well as lost revenue from electricity sales. The most effective means of minimizing O&M costs is to improve reliability. This paper examines the consequences of applying maintenance strategies on O&M costs. Applying this integrated approach in reliability analysis can contribute to costs and revenues trade-off.