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Maintenance optimization for offshore wind turbines using POMDP

机译：使用POMDP优化海上风机的维护

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摘要

In this work, a Partially Observable Markov Decision Process (POMDP) is used for decision support for offshore wind turbines. The optimal decision policies for inspection and repair are obtained for each time step dependent on the belief state for the damage state after monitoring, and thus dynamic programming can be used. Seasonal weather variations are included through their influence on weather constrains for inspections and repairs, as well as costs to lost production, when corrective repair is not possible after failure. Application of the model is illustrated through an example, where the main bearing is considered. Optimization is initially performed for one component, and decision making for an entire wind farm is considered by using revised decision policies, when mobilization costs are already paid for another repair. The total costs are calculated for an entire wind farm using simulation and by using the identified decision policies.
机译：在这项工作中，部分可观察的马尔可夫决策过程（POMDP）用于海上风力涡轮机的决策支持。根据监视后的损坏状态的置信状态，针对每个时间步获得用于检查和维修的最佳决策策略，因此可以使用动态编程。当故障后无法进行纠正性维修时，季节性天气变化会因为其对检查和维修的天气约束的影响以及生产损失的成本而包括在内。通过示例说明了模型的应用，其中考虑了主轴承。最初对一个组件进行优化，然后在已经为另一项维修支付了动员成本的情况下，使用修订后的决策策略来考虑整个风电场的决策。使用模拟并使用确定的决策策略来计算整个风电场的总成本。

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