A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilizing an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different subgroups, representing (1) use-type, (2) component, or (3) 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 (15% and 13% increases in predictive log-likelihood of hazard) and power prediction in a wind farm (up to 82% reduction in the standard deviation of maximum output prediction). 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 subfleets are allowed to share correlated information at different levels in the hierarchy; the (averaged) reduction in standard deviation for interpretable parameters in the survival analysis is 70%, alongside 32% in wind farm power models. 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). Successes in both case studies demonstrate the wide applicability in practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples.

中文翻译：

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通过多任务学习跨工程车队进行知识转移的分层贝叶斯建模

在为工程基础设施构建预测模型时，提出了人口水平分析来解决数据稀疏性问题。利用可解释的分层贝叶斯方法和运营车队数据，领域专业知识在不同子组之间自然编码（并适当共享），代表（1）使用类型，（2）组件或（3）操作条件。具体来说，利用领域专业知识通过假设（和先验分布）来约束模型，允许该方法在类似资产之间自动共享信息，改进卡车车队的生存分析（危险的预测对数似然增加 15% 和 13% ) 和风电场的功率预测（最大输出预测的标准偏差减少高达 82%）。在每个资产管理示例中，在组合推理中，通过舰队学习一组相关函数，以学习种群模型。当允许子舰队在层次结构的不同级别共享相关信息时，参数估计得到改进；生存分析中可解释参数的标准差（平均）减少了 70%，而风力发电模型则减少了 32%。反过来，数据不完整的群体会自动从数据丰富的群体那里借用统计优势。统计相关性通过贝叶斯转移学习实现知识转移，并且可以检查相关性以告知哪些资产共享信息的影响（即参数）。这两个案例研究的成功证明了在实际基础设施监测中的广泛适用性，