论文标题
最佳运输的空间异常检测
Spatial anomaly detection with optimal transport
论文作者
论文摘要
该手稿概述了针对喷气发动机的自动异常检测框架。它针对发动机的各个轴向站点的稳态温度测量中的空间异常进行了定制。该框架基于最佳运输理论的高斯措施的思想，这些措施为瓦斯恒星距离和barycenters提供了分析解决方案。提出的异常检测框架建立在我们先前的努力之上，将温度的空间分布视为高斯随机场。我们通过在一个发动机家族的数据集上进行培训，并将其应用于许多发动机中，从而证明了我们方法的实用性 - 成功地检测出异常情况，同时避免了误报和虚假负面因素。尽管本文考虑的主要应用是发动机中的温度测量值，但对其他内部流和相关热力学量的应用被清除。
This manuscript outlines an automated anomaly detection framework for jet engines. It is tailored for identifying spatial anomalies in steady-state temperature measurements at various axial stations in an engine. The framework rests upon ideas from optimal transport theory for Gaussian measures which yields analytical solutions for both Wasserstein distances and barycenters. The anomaly detection framework proposed builds upon our prior efforts that view the spatial distribution of temperature as a Gaussian random field. We demonstrate the utility of our approach by training on a dataset from one engine family, and applying them across a fleet of engines -- successfully detecting anomalies while avoiding both false positives and false negatives. Although the primary application considered in this paper are the temperature measurements in engines, applications to other internal flows and related thermodynamic quantities are made lucid.
