A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilising an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different sub-groups, representing (i) use-type, (ii) component, or (iii) operating condition. Specifically, domain expertise is exploited to constrain the model via assumptions (and prior distributions) allowing the methodology to automatically share information between similar assets, improving the survival analysis of a truck fleet and power prediction in a wind farm. In each asset management example, a set of correlated functions is learnt over the fleet, in a combined inference, to learn a population model. Parameter estimation is improved when sub-fleets share correlated information at different levels of the hierarchy. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The statistical correlations enable knowledge transfer via Bayesian transfer learning, and the correlations can be inspected to inform which assets share information for which effect (i.e. parameter). Both case studies demonstrate the wide applicability to practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples.
翻译:提议进行人口层面分析,以解决在为工程基础设施建立预测模型时的数据偏僻性; 利用可解释的贝耶斯等级方法和操作船队数据,在代表(一) 使用类型、(二) 组成部分或(三) 运行条件的不同分组之间自然编码(和适当分享)域专门知识,具体地说,利用域专门知识限制模型,办法是假设(和先前的分布),使该方法能够在类似资产之间自动共享信息,改进卡车车队的生存分析和风力农场电力预测; 在每个资产管理实例中,通过综合推断,在车队中学习一套相互关联的功能,学习人口模型; 在分层不同级别分享相关信息时,参数估计得到改进; 而数据不完整的分组则自动从数据丰富者那里借用统计力量; 统计相关性使得通过Bayesian转移学习知识的转移,以及可以检查关联性,以告知哪些资产共享效果的信息(例如参数); 两种案例研究都表明,在实际基础设施监测上的广泛适用性,因为该方法可以自然地解释不同的车队模型。