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：针对工程基础设施预测模型构建中的数据稀疏性问题，本文提出一种群体级分析方法。通过采用可解释的分层贝叶斯方法及运行机群数据，领域知识得以在不同子群（分别代表(1)使用类型、(2)部件或(3)运行工况）之间自然编码并适当共享。具体而言，该方法利用领域知识，通过假设（及先验分布）对模型进行约束，使得不同相似资产间的信息自动共享，从而提升卡车机群的生存分析精度与风电场的功率预测效果。在每个资产管理案例中，通过联合推理学习机群上一组相关函数，从而获得群体模型。当子机群在层级不同层次共享相关信息时，参数估计精度得到提升；同时，数据不完整的群体可自动从数据丰富的群体中借取统计强度。这种统计相关性通过贝叶斯迁移学习实现知识迁移，且可通过对相关性进行检验，明确哪些资产针对何种效应（即参数）共享信息。两项案例研究均证明了该方法在基础设施实际监测中的广泛适用性，因其能根据不同现场实例中可解释的机群模型实现自然自适应调整。