A machine-learning phase classification scheme for anomaly detection in signals with periodic characteristics

Lia Ahrens, Julian Ahrens, Hans Dieter Schotten

In: EURASIP Journal on Advances in Signal Processing (ASP) 2019 27 Seiten 1-23 Springer International Publishing 5/2019.


In this paper, we propose a novel machine-learning method for anomaly detection applicable to data with periodic characteristics where randomly varying period lengths are explicitly allowed. A multi-dimensional time series analysis is conducted by training a data-adapted classifier consisting of deep convolutional neural networks performing phase classification. The entire algorithm including data pre-processing, period detection, segmentation, and even dynamic adjustment of the neural networks is implemented for fully automatic execution. The proposed method is evaluated on three example datasets from the areas of cardiology, intrusion detection, and signal processing, presenting reasonable performance.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence