Szczegóły publikacji
Opis bibliograficzny
Explainable anomaly detection for Hot-rolling industrial process / Jakub JAKUBOWSKI, Przemysław Stanisz, Szymon Bobek, Grzegorz J. Nalepa // W: DSAA'2021 [Dokument elektroniczny] : 8th international conference on Data Science and Advanced Analytics : 6–9 October 2021, Porto, Portugal : proceedings. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2021. — e-ISBN: 978-1-6654-2099-0. — S. [1–10]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [10], Abstr. — J. Jakubowski - dod. afiliacja: Arcelor Mittal Poland
Autorzy (4)
- AGHJakubowski Jakub
- Stanisz Przemysław
- Bobek Szymon
- Nalepa Grzegorz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 139111 |
|---|---|
| Data dodania do BaDAP | 2022-02-16 |
| Tekst źródłowy | URL |
| DOI | 10.1109/DSAA53316.2021.9564228 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Conference on Data Science and Advanced Analytics 2021 |
Abstract
Anomaly detection is emerging trend in manufacturing processes and may be considered as part of the Industry 4.0 revolution. It can serve both as diagnostic tool in predictive maintenance task, as well as trace back mechanism for assessing quality of production or services. In this paper we describe and approach for explainable anomaly detection in industrial data which contains sequential and static features. We based our solution on modified autoencoder architecture with Long Short-Term Memory layers. To address a problem of explinability in deep learning and find origin of the anomalies we have engaged the SHAP method, which gives both local and global explanations of the model. Analysis of SHAP explanations allowed us to determine the source of majority of anomalies detected by deep learning model. We demonstrated the feasibility of our approach on synthetic, reproducible dataset and on real-life data gathered from hot rolling industrial process.
