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https://doi.org/10.21256/zhaw-25296| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data |
| Autor/-in: | Zgraggen, Jannik Pizza, Gianmarco Goren Huber, Lilach |
| et. al: | No |
| DOI: | 10.36001/phme.2022.v7i1.3342 10.21256/zhaw-25296 |
| Tagungsband: | Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022 |
| Herausgeber/-in des übergeordneten Werkes: | Do, Phuc Michau, Gabriel Ezhilarasu, Cordelia |
| Band(Heft): | 7 |
| Heft: | 1 |
| Seite(n): | 530 |
| Seiten bis: | 540 |
| Angaben zur Konferenz: | 7th European PHM, Turin, Italy, 6-8 July 2022 |
| Erscheinungsdatum: | 4-Jul-2022 |
| Reihe: | PHM Society European Conference |
| Reihenzählung: | 7 |
| Verlag / Hrsg. Institution: | PHM Society |
| Verlag / Hrsg. Institution: | State College |
| ISBN: | 978-1-936263-36-3 |
| Sprache: | Englisch |
| Schlagwörter: | Deep learning; Anomaly detection; Predictive maintenance; Wind turbines; Uncertainty quantification; Condition based maintenance; Machine learning |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Quantifying the predictive uncertainty of a model is an important ingredient in data-driven decision making. Uncertainty quantification has been gaining interest especially for deep learning models, which are often hard to justify or explain. Various techniques for deep learning based uncertainty estimates have been developed primarily for image classification and segmentation, but also for regression and forecasting tasks. Uncertainty quantification for anomaly detection tasks is still rather limited for image data and has not yet been demonstrated for machine fault detection in PHM applications. In this paper we suggest an approach to derive an uncertainty-informed anomaly score for regression models trained with normal data only. The score is derived using a deep ensemble of probabilistic neural networks for uncertainty quantification. Using an example of wind-turbine fault detection, we demonstrate the superiority of the uncertainty-informed anomaly score over the conventional score. The advantage is particularly clear in an "out-of-distribution" scenario, in which the model is trained with limited data which does not represent all normal regimes that are observed during model deployment. |
| Weitere Angaben: | Best Paper Award Lizenzangabe: CC BY 3.0 United States |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/25296 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 3.0: Namensnennung 3.0 Unported |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Publiziert im Rahmen des ZHAW-Projekts: | Machine Learning Based Fault Detection for Wind Turbines |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2022_Zgraggen-Pizza-Goren-Huber_Uncertainty-informed_ProPHME22.pdf | 3.56 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Zgraggen, J., Pizza, G., & Goren Huber, L. (2022). Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data [Conference paper]. In P. Do, G. Michau, & C. Ezhilarasu (Eds.), Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022 (Vol. 7, Issue 1, pp. 530–540). PHM Society. https://doi.org/10.36001/phme.2022.v7i1.3342
Zgraggen, J., Pizza, G. and Goren Huber, L. (2022) ‘Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data’, in P. Do, G. Michau, and C. Ezhilarasu (eds) Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022. State College: PHM Society, pp. 530–540. Available at: https://doi.org/10.36001/phme.2022.v7i1.3342.
J. Zgraggen, G. Pizza, and L. Goren Huber, “Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data,” in Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022, Jul. 2022, vol. 7, no. 1, pp. 530–540. doi: 10.36001/phme.2022.v7i1.3342.
ZGRAGGEN, Jannik, Gianmarco PIZZA und Lilach GOREN HUBER, 2022. Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data. In: Phuc DO, Gabriel MICHAU und Cordelia EZHILARASU (Hrsg.), Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022. Conference paper. State College: PHM Society. 4 Juli 2022. S. 530–540. ISBN 978-1-936263-36-3
Zgraggen, Jannik, Gianmarco Pizza, and Lilach Goren Huber. 2022. “Uncertainty Informed Anomaly Scores with Deep Learning : Robust Fault Detection with Limited Data.” Conference paper. In Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022, edited by Phuc Do, Gabriel Michau, and Cordelia Ezhilarasu, 7:530–40. State College: PHM Society. https://doi.org/10.36001/phme.2022.v7i1.3342.
Zgraggen, Jannik, et al. “Uncertainty Informed Anomaly Scores with Deep Learning : Robust Fault Detection with Limited Data.” Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022, edited by Phuc Do et al., vol. 7, no. 1, PHM Society, 2022, pp. 530–40, https://doi.org/10.36001/phme.2022.v7i1.3342.
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