Szczegóły publikacji
Opis bibliograficzny
Explainable anomaly detection in industrial streams / Jakub JAKUBOWSKI, Przemysław Stanisz, Szymon Bobek, Grzegorz J. Nalepa // W: Artificial intelligence : ECAI 2023 international workshop : XAI${^{\wedge}}$3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI : Kraków, Poland, September 30 – October 4, 2023 : proceedings, Pt. 1 / eds. Sławomir Nowaczyk, Przemysław Biecek, Neo Christopher Chung, Mauro Vallati, Paweł Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomáš Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczysław Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova. — Cham : Springer Nature Switzerland, cop. 2024. — (Communications in Computer and Information Science ; ISSN 1865-0929 ; CCIS 1947). — ISBN: 978-3-031-50395-5; e-ISBN: 978-3-031-50396-2. — S. 87–100. — Bibliogr., Abstr. — Publikacja dostępna online od: 2024-01-21. — J. Jakubowski – dod. afiliacja ArcelorMittal Poland, Kraków, Polska
Autorzy (4)
- AGHJakubowski Jakub
- Stanisz Przemysław
- Bobek Szymon
- Nalepa Grzegorz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151688 |
|---|---|
| Data dodania do BaDAP | 2024-03-13 |
| DOI | 10.1007/978-3-031-50396-2_5 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | European Conference on Artificial Intelligence 2023 |
| Czasopismo/seria | Communications in Computer and Information Science |
Abstract
Anomaly detection in industrial environment is a complex task, which requires to consider multiple characteristics of the data from industrial sensors and anomalies itself. Such data is often highly imbalanced and the availability of labels is limited. The data is generated in streaming fashion, which means that it is unbounded and potentially infinite. The industrial process may evolve over time due to degradation of the asset, maintenance actions or modifications. The manual verification and definition of anomaly source is a tideous task, which requires human to carefully investigate each anomalous observation. An anomaly detection system should consider all above challanges. In this paper we propose a system, which addresses the discussed issues. It is applicable for industrial data stream scenarios and comprises of unsupervised anomaly detection model, resampling module and explanation module. We consider two different approaches towards the utilization of machine learning model – online and offline. We present our work in relation to a cold rolling process use case, which is one of the steps in production of steel strips.
