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- W4285483702 abstract "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." @default.
- W4285483702 created "2022-07-15" @default.
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- W4285483702 date "2022-07-13" @default.
- W4285483702 modified "2023-09-27" @default.
- W4285483702 title "Spatial anomaly detection with optimal transport" @default.
- W4285483702 doi "https://doi.org/10.48550/arxiv.2207.06166" @default.
- W4285483702 hasPublicationYear "2022" @default.
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