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

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

Abstract 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 consisti...

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Bibliographic Details
Main Authors: Lia Ahrens, Julian Ahrens, Hans D. Schotten
Format: Article
Language:English
Published: SpringerOpen 2019-05-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Anomaly detection
Time series analysis
Phase classification
Machine learning
Convolutional neural networks
Online Access:http://link.springer.com/article/10.1186/s13634-019-0619-3

Internet

http://link.springer.com/article/10.1186/s13634-019-0619-3

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