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Unsupervised Approaches in Anomaly Detection

Autor: 
Bermejo Higuera, Juan Ramón
;
Bermejo Higuera, Javier
;
Sicilia Moltalvo, Juan Antonio
;
González Crespo, Rubén
Fecha: 
2024
Palabra clave: 
Revista / editorial: 
Springer
Citación: 
Higuera, J.R.B., Higuera, J.B., Montalvo, J.A.S., Crespo, R.G. (2024). Unsupervised Approaches in Anomaly Detection. In: Nayak, J., Naik, B., S, V., Favorskaya, M. (eds) Machine Learning for Cyber Physical System: Advances and Challenges. Intelligent Systems Reference Library, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-54038-7_3
Tipo de Ítem: 
bookPart
URI: 
https://reunir.unir.net/handle/123456789/19558
DOI: 
https://doi.org/10.1007/978-3-031-54038-7_3
Dirección web: 
https://link.springer.com/chapter/10.1007/978-3-031-54038-7_3
Resumen:
[Resumen no disponible]
Descripción: 
Industry 4.0 is a new industrial stage based on the revolution brought about by the integration of information and communication technologies (ICT) in conventional manufacturing systems, leading to the implementation of cyber-physical systems. With Industry 4.0 and cyber-physical systems, the number of sensors and thus the data from the monitoring of manufacturing machines is increasing. This implies an opportunity to leverage this data to improve production efficiency. One of these ways is by using it to detect unusual patterns, which can allow, among other things, the detection of machine malfunctions or cutting tool wear. In addition, this information can then be used to better schedule maintenance tasks and make the best possible use of resources. In this chapter, we will study unsupervised clustering techniques and others such as nearest neighbor methods or statistical techniques for anomaly detection that can be applied to machining process monitoring data.
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