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Self-organizing maps for anomaly localization and predictive maintenance in cyber-physical production systems

: Birgelen, Alexander von; Buratti, Davide; Mager, Jens; Niggemann, Oliver

Fulltext urn:nbn:de:0011-n-5067888 (1.1 MByte PDF)
MD5 Fingerprint: 9b0e36ec3d74b8b64291342f3477b227
(CC) by-nc-nd
Created on: 24.8.2018

Procedia CIRP 72 (2018), pp.480-485
ISSN: 2212-8271
Conference on Manufacturing Systems (CMS) <51, 2018, Stockholm>
Journal Article, Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Modern Cyber-Physical Production Systems provide large amounts of data such as sensor and control signals or configuration parameters. The available data enables unsupervised, data-driven solutions for model-based anomaly detection, anomaly localization and predictive maintenance: models which represent the normal behaviour of the system are learned from data. Then, live data from the system can be compared to the predictions of the model to detect faults, perform fault diagnosis and derive the overall condition of a system or its components. In this paper we use self-organizing maps for the aforementioned tasks and evaluate the presented methods on several real-world systems.