Robust Functional Regression for Outlier Detection

Hullait, H. and Leslie, D.S. and Pavlidis, N.G. and King, S. (2020) Robust Functional Regression for Outlier Detection. In: Advanced Analytics and Learning on Temporal Data. Lecture Notes in Computer Science . Springer, Cham, pp. 3-13. ISBN 9783030390976

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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.

Item Type:
Contribution in Book/Report/Proceedings
Subjects:
ID Code:
163323
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Deposited On:
10 Dec 2021 17:47
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Apr 2022 10:55