Bayesian network models for making maintenance decisions from data and expert judgment
Bayesian network models for making maintenance decisions from data and expert judgment
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用于根据数据和专家判断做出维护决策的贝叶斯网络模型
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Haoyuan Zhang
中科院分区：
文献类型：
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作者：
D. Marsh;Haoyuan Zhang
为了使资产可靠性经济有效地最大化，维护应该基于资产可能的劣化来安排。已经提出了许多类型的统计模型来预测这一点，但它们具有重要的实际局限性。我们提出了一个贝叶斯网络模型，可以用于维护决策支持，以克服这些限制。该模型扩展了现有的资产劣化统计模型，但展示了如何i)来自相关资产组的故障数据可以组合，ii)从定期检查中获得的资产状况数据可以使用，iii)劣化原因的专家知识可以与统计数据相结合以调整预测，以及iv)维护行动的不确定影响可以建模。我们展示了该模型如何用于一系列决策问题，给出了在实践中可能可用的典型数据。
To maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. A number of types of statistical model have been proposed for predicting this but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how i) failure data from related groups of asset can be combined, ii) data on the condition of assets available from their periodic inspection can be used iii) expert knowledge of the causes deterioration can be combined with statistical data to adjust predictions and iv) the uncertain effects of maintenance actions can be modelled. We show how the model could be used for a range of decision problems, given typical data likely to be available in practice.