Self-Organizing Maps for Anomaly Localization and Predictive Maintenance in Cyber-Physical Production Systems

Self-Organizing Maps for Anomaly Localization and Predictive Maintenance in Cyber-Physical Production Systems. von Birgelen, A., Buratti, D., Mager, J., & Niggemann, O. Procedia CIRP, 72:480–485, January, 2018.

Paper doi abstract bibtex

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.

@article{von_birgelen_self-organizing_2018,
	series = {51st {CIRP} {Conference} on {Manufacturing} {Systems}},
	title = {Self-{Organizing} {Maps} for {Anomaly} {Localization} and {Predictive} {Maintenance} in {Cyber}-{Physical} {Production} {Systems}},
	volume = {72},
	issn = {2212-8271},
	url = {https://www.sciencedirect.com/science/article/pii/S221282711830307X},
	doi = {10.1016/j.procir.2018.03.150},
	abstract = {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.},
	language = {en},
	urldate = {2022-05-16},
	journal = {Procedia CIRP},
	author = {von Birgelen, Alexander and Buratti, Davide and Mager, Jens and Niggemann, Oliver},
	month = jan,
	year = {2018},
	keywords = {CPPS, SOM, anomaly detection, anomaly localization, cyber-physical production system, data-driven, diagnosis, predictive maintenance, self-organizing map},
	pages = {480--485},
}

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