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Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder

: Eiteneuer, Benedikt; Hranisavljevic, Nemanja; Niggemann, Oliver


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-; Federation University Australia:
IEEE International Conference on Industrial Technology, ICIT 2019. Proceedings : Melbourne, Australia, 13-15 February 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-5386-6376-9
ISBN: 978-1-5386-6375-2
ISBN: 978-1-5386-6377-6
International Conference on Industrial Technology (ICIT) <20, 2019, Melbourne>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Conference Paper
Fraunhofer IOSB ()

Unsupervised anomaly detection (AD) is a major topic in the field of Cyber-Physical Production Systems (CPPSs). A closely related concern is dimensionality reduction (DR) which is: 1) often used as a preprocessing step in an AD solution, 2) a sort of AD, if a measure of observation conformity to the learned data manifold is provided. We argue that the two aspects can be complementary in a CPPS anomaly detection solution. In this work, we focus on the nonlinear autoencoder (AE) as a DR/AD approach. The contribution of this work is: 1) we examine the suitability of AE reconstruction error as an AD decision criterion in CPPS data. 2) we analyze its relation to a potential second-phase AD approach in the AE latent space 3) we evaluate the performance of the approach on three real-world datasets. Moreover, the approach outperforms state-of-the-art techniques, alongside a relatively simple and straightforward application.