Data from populations of systems are prevalent in many industrial applications. Machines and infrastructure are increasingly instrumented with sensing systems, emitting streams of telemetry data with complex interdependencies. In practice, data-centric monitoring procedures tend to consider these assets (and respective models) as distinct -- operating in isolation and associated with independent data. In contrast, this work captures the statistical correlations and interdependencies between models of a group of systems. Utilising a Bayesian multilevel approach, the value of data can be extended, since the population can be considered as a whole, rather than constituent parts. Most interestingly, domain expertise and knowledge of the underlying physics can be encoded in the model at the system, subgroup, or population level. We present an example of acoustic emission (time-of-arrival) mapping for source location, to illustrate how multilevel models naturally lend themselves to representing aggregate systems in engineering. In particular, we focus on constraining the combined models with domain knowledge to enhance transfer learning and enable further insights at the population level.

翻译：来自系统群体的数据在许多工业应用中普遍存在。机器和基础设施日益配备传感系统，发射出具有复杂相互依赖关系的遥测数据流。在实践中，以数据为中心的监控流程往往将这些资产（及其相应模型）视为独立个体——孤立运行并关联独立数据。相反，本工作捕捉了一组系统模型中各模型间的统计相关性和相互依赖性。利用贝叶斯多水平方法，数据的价值得以扩展，因为可将群体视为一个整体而非组成部分。最有趣的是，领域专业知识和底层物理知识可以编码到系统、子群或群体层面的模型中。我们以声发射（到达时间）映射进行源定位为例，说明多水平模型如何自然地适用于表示工程中的聚合系统。特别地，我们重点探讨如何利用领域知识约束组合模型，以增强迁移学习能力，并在群体层面获得更深入的洞见。