We propose a population-level analysis to address issues of data sparsity when building predictive models of engineering infrastructure. By sharing information between similar assets, hierarchical Bayesian modelling is used to improve the survival analysis of a truck fleet (hazard curves) and power prediction in a wind farm (power curves). In each example, a set of correlated functions are learnt over the asset fleet, in a combined inference, to learn a population model. Parameter estimation is improved when sub-fleets of assets are allowed to share correlated information at different levels in the hierarchy. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The correlations can be inspected to inform which assets share information for which effect (i.e. parameter).
翻译:我们建议进行人口层面分析,以解决在建立工程基础设施预测模型时的数据宽度问题;通过在类似资产之间共享信息,使用贝叶斯人等级模型改进卡车车队(危险曲线)的生存分析以及风力农场(电力曲线)的电力预测;在每个例子中,通过综合推理,对资产船队学习一套相关功能,以学习人口模型;当允许资产分档在等级体系的不同级别共享相关信息时,参数估算得到改进;反过来,数据不完整的人群自动从数据丰富的群体中借取统计力量;可以对相关关系进行检查,以告知哪些资产共享其效果(即参数)的信息。