Abstract
The bad working environment and various complex influencing factors lead to the high failure rate of a wind turbine. It is necessary to decide on a reasonable maintenance strategy for wind turbines. The classification of wind turbine components based on their importance degrees can identify the components which have an important impact on the reliability and maintenance of wind turbines. However, the traditional multi-criteria decision-making method can only provide the importance ranking of components, rather than the importance classification of components. The emergence of the three-way decision (TWD) method makes up for this deficiency. Then we classify the importance degrees of components by the TWD method. Firstly, the decision-theoretic rough sets are introduced into the uncertain linguistic setting to construct uncertain linguistic decision-theoretic rough sets. Secondly, the weights of experts are attained based on the consistency degree of loss functions. Thirdly, conditional probability is attained by the evaluation based on distance from the average solution method. And the classification of wind turbine components is derived based on the minimum-loss principle. Finally, a case study about the classification of the wind turbine components based on importance degrees is employed to certify the practicability of our designed method. The proposed model extends both the theory and practice of TWD and offers a classification helpful to make maintenance strategies for reducing the risk of component failure.
•It employs the three-way decision to the classification of the components.•It attempts to use ULVs to describe the uncertain information in TWD.•It takes the appraisal score attained by the EDAS as the conditional probability.