An Automated ML Anomaly Detection Prototype
Predictive maintenance (PdM) often fails to progress beyond pilot projects because machine learning-based anomaly detection requires expert knowledge, extensive tuning, and labeled fault data. This paper presents an automated prototype that builds and evaluates multiple anomaly detection models with...
Gardado en:
| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
MDPI AG
2025-12-01
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| Series: | Applied Sciences |
| Assuntos: | |
| Acceso en liña: | https://www.mdpi.com/2076-3417/16/1/337 |
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