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Robust Functional Regression for Outlier Detection

  • 2020
  • OriginalPaper
  • Chapter
Published in:
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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next chapter Transform Learning Based Function Approximation for Regression and Forecasting
Title
Robust Functional Regression for Outlier Detection
Authors
Harjit Hullait
David S. Leslie
Nicos G. Pavlidis
Steve King
Copyright Year
2020
Book
Advanced Analytics and Learning on Temporal Data

Print ISBN: 978-3-030-39097-6

Electronic ISBN: 978-3-030-39098-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39098-3_1
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