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2020 | OriginalPaper | Chapter

Robust Functional Regression for Outlier Detection

Authors : Harjit Hullait, David S. Leslie, Nicos G. Pavlidis, Steve King

Published in: Advanced Analytics and Learning on Temporal Data

Publisher: Springer International Publishing

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Abstract

In this paper we propose an outlier detection algorithm for temperature sensor data from jet engine tests. Effective identification of outliers would enable engine problems to be examined and resolved efficiently. Outlier detection in this data is challenging because a human controller determines the speed of the engine during each manoeuvre. This introduces variability which can mask abnormal behaviour in the engine response. We therefore suggest modelling the dependency between speed and temperature in the process of identifying abnormalities. The engine temperature has a delayed response with respect to the engine speed, which we will model using robust functional regression. We then apply functional depth with respect to the residuals to rank the samples and identify the outliers. The effectiveness of the outlier detection algorithm is shown in a simulation study. The algorithm is also applied to real engine data, and identifies samples that warrant further investigation.
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Metadata
Title
Robust Functional Regression for Outlier Detection
Authors
Harjit Hullait
David S. Leslie
Nicos G. Pavlidis
Steve King
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-39098-3_1

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