An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments †

Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing operators to take appropriate correctiv...

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Detalles Bibliográficos
Autores principales: Antonini, Mattia, Pincheira, Miguel, Vecchio, Massimo, Antonelli, Fabio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962960/
https://www.ncbi.nlm.nih.gov/pubmed/36850940
http://dx.doi.org/10.3390/s23042344

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962960/
https://www.ncbi.nlm.nih.gov/pubmed/36850940
http://dx.doi.org/10.3390/s23042344

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