Application of multivariate outlier detection to fluid velocity measurements

Application of multivariate outlier detection to fluid velocity measurements A statistical-based approach to detect outliers in fluid-based velocity measurements is proposed. Outliers are effectively detected from experimental unimodal distributions with the application of an existing multivariate outlier detection algorithm for asymmetric distributions (Hubert and Van der Veeken, J Chemom 22:235–246, 2008). This approach is an extension of previous methods that only apply to symmetric distributions. For fluid velocity measurements, rejection of statistical outliers, meaning erroneous as well as low probability data, via multivariate outlier rejection is compared to a traditional method based on univariate statistics. For particle image velocimetry data, both tests are conducted after application of the current de facto standard spatial filter, the universal outlier detection test (Westerweel and Scarano, Exp Fluids 39:1096–1100, 2005). By doing so, the utility of statistical outlier detection in addition to spatial filters is demonstrated, and further, the differences between multivariate and univariate outlier detection are discussed. Since the proposed technique for outlier detection is an independent process, statistical outlier detection is complementary to spatial outlier detection and can be used as an additional validation tool. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

Application of multivariate outlier detection to fluid velocity measurements

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Publisher
Springer-Verlag
Copyright
Copyright © 2010 by Springer-Verlag
Subject
Engineering; Engineering Thermodynamics, Heat and Mass Transfer; Fluid- and Aerodynamics; Engineering Fluid Dynamics
ISSN
0723-4864
eISSN
1432-1114
D.O.I.
10.1007/s00348-010-0875-3
Publisher site
See Article on Publisher Site

Abstract

A statistical-based approach to detect outliers in fluid-based velocity measurements is proposed. Outliers are effectively detected from experimental unimodal distributions with the application of an existing multivariate outlier detection algorithm for asymmetric distributions (Hubert and Van der Veeken, J Chemom 22:235–246, 2008). This approach is an extension of previous methods that only apply to symmetric distributions. For fluid velocity measurements, rejection of statistical outliers, meaning erroneous as well as low probability data, via multivariate outlier rejection is compared to a traditional method based on univariate statistics. For particle image velocimetry data, both tests are conducted after application of the current de facto standard spatial filter, the universal outlier detection test (Westerweel and Scarano, Exp Fluids 39:1096–1100, 2005). By doing so, the utility of statistical outlier detection in addition to spatial filters is demonstrated, and further, the differences between multivariate and univariate outlier detection are discussed. Since the proposed technique for outlier detection is an independent process, statistical outlier detection is complementary to spatial outlier detection and can be used as an additional validation tool.

Journal

Experiments in FluidsSpringer Journals

Published: Apr 14, 2010

References

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