Multivariate Time Series Anomaly Detection in Industry 5.0
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908276023623680 |
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| author | Colombi, Lorenzo Vespa, Michela Belletti, Nicolas Brina, Matteo Dahdal, Simon Tabanelli, Filippo Bellodi, Elena Tortonesi, Mauro Stefanelli, Cesare Vignoli, Massimiliano |
| author_facet | Colombi, Lorenzo Vespa, Michela Belletti, Nicolas Brina, Matteo Dahdal, Simon Tabanelli, Filippo Bellodi, Elena Tortonesi, Mauro Stefanelli, Cesare Vignoli, Massimiliano |
| contents | Industry5.0 environments present a critical need for effective anomaly detection methods that can indicate equipment malfunctions, process inefficiencies, or potential safety hazards. The ever-increasing sensorization of manufacturing lines makes processes more observable, but also poses the challenge of continuously analyzing vast amounts of multivariate time series data. These challenges include data quality since data may contain noise, be unlabeled or even mislabeled. A promising approach consists of combining an embedding model with other Machine Learning algorithms to enhance the overall performance in detecting anomalies. Moreover, representing time series as vectors brings many advantages like higher flexibility and improved ability to capture complex temporal dependencies. We tested our solution in a real industrial use case, using data collected from a Bonfiglioli plant. The results demonstrate that, unlike traditional reconstruction-based autoencoders, which often struggle in the presence of sporadic noise, our embedding-based framework maintains high performance across various noise conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multivariate Time Series Anomaly Detection in Industry 5.0 Colombi, Lorenzo Vespa, Michela Belletti, Nicolas Brina, Matteo Dahdal, Simon Tabanelli, Filippo Bellodi, Elena Tortonesi, Mauro Stefanelli, Cesare Vignoli, Massimiliano Machine Learning Industry5.0 environments present a critical need for effective anomaly detection methods that can indicate equipment malfunctions, process inefficiencies, or potential safety hazards. The ever-increasing sensorization of manufacturing lines makes processes more observable, but also poses the challenge of continuously analyzing vast amounts of multivariate time series data. These challenges include data quality since data may contain noise, be unlabeled or even mislabeled. A promising approach consists of combining an embedding model with other Machine Learning algorithms to enhance the overall performance in detecting anomalies. Moreover, representing time series as vectors brings many advantages like higher flexibility and improved ability to capture complex temporal dependencies. We tested our solution in a real industrial use case, using data collected from a Bonfiglioli plant. The results demonstrate that, unlike traditional reconstruction-based autoencoders, which often struggle in the presence of sporadic noise, our embedding-based framework maintains high performance across various noise conditions. |
| title | Multivariate Time Series Anomaly Detection in Industry 5.0 |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.15946 |