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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2023
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| Materias: | |
| 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 |