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Failure Risk Indicators for a Maintenance Model Based on Observable Life of Industrial Components With an Application to Wind Turbines

机译:基于工业组件可观察寿命的维护模型故障风险指标及其在风力发电机组中的应用

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This paper presents a new method able to estimate the health condition of components in a wind turbine based on the on-line information collected about their observable lives. The proposed method uses the information coming in real-time to characterize risk indicators for failure modes of the main components of a wind turbine operating under different normal conditions. The estimation of these risk indicators is based on normal behaviour models previously fitted with real data about the typical life of a component carrying out its functions within its own environment. The maintenance plan applied to the components of a wind turbine can be dynamically rescheduled according to the observed values of the risk indicators in a component using the resources that are really needed. Two approaches are presented to determine thresholds for alerting about risky health conditions: a maximum limit that the risk indicator should not overpass according to its life condition, and technical and economical feasibility. These approaches are the main foundations for a new maintenance model able to integrate in a natural way different information coming from the operation and maintenance of a component, and so capable of maximising the lifespan of the asset. Some real examples of the application of these new concepts in components of a wind turbine will be described.
机译:本文提出了一种新方法,该方法能够基于收集的有关其可观察寿命的在线信息来估计风力涡轮机中组件的健康状况。所提出的方法使用实时获得的信息来表征在不同正常条件下运行的风力涡轮机主要组件的故障模式的风险指标。这些风险指标的估算基于正常行为模型,该模型先前装有有关组件在其自身环境中执行其功能的典型寿命的真实数据。可以使用实际需要的资源,根据组件中风险指标的观察值动态地重新计划应用于风力涡轮机组件的维护计划。提出了两种方法来确定有关危险健康状况的警报阈值:风险指标根据其生命状况不应超过的最大限制以及技术和经济可行性。这些方法是新维护模型的主要基础,该维护模型能够以自然的方式集成来自组件操作和维护的不同信息,从而能够最大化资产的使用寿命。将描述在风力涡轮机的组件中应用这些新概念的一些实际示例。

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