Szczegóły publikacji
Opis bibliograficzny
Deep Learning damage forecasting for rotating devices using Bayesian sub-predictors / Paweł KNAP, Patryk BAŁAZY, Szymon PODLASEK // W: Mechatronics – Industry-Inspired Advances / eds. Adam Martowicz, Michał Mańka, Krzysztof Mendrok. — Cham : Springer Nature, cop. 2024. — (Lecture Notes in Networks and Systems ; ISSN 2367-3370 ; vol. 1042). — Materiały z konferencji 6th International Conference Mechatronics 2023: Ideas for Industrial Applications : 11-13 December 2023, Krakow, Poland. — ISBN: 978-3-031-63443-7; e-ISBN: 978-3-031-63444-4. — S. 15–28. — Bibliogr. s. 27–28, Abstr. — Publikacja dostępna online od: 2024-06-27
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 154165 |
|---|---|
| Data dodania do BaDAP | 2024-07-15 |
| DOI | 10.1007/978-3-031-63444-4_2 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes in Networks and Systems |
Abstract
Predictive maintenance is a growing element of the Industry 4.0 strategy, and it involves detecting the possibility of failure as soon as possible. Very often, vibration data, appropriately reprocessed using such signal analysis methods as Fourier transform or wavelet transform, is used to detect drive failures. This paper presents the results of a study of a neural Deep Learning classifier system. The problem arising from the occurrence of noise in the learning and validation data is defined. It this method a use of Bayesian subpredictor was proposed to marginalize noise influence on the networks learning and validation performance. The designed classifier with Bayesian sub predictor achieved an accuracy of 96.08% on the test set. The proposed solution can be effectively applied to wind turbines, water turbines and many other rotating machines present in modern industry.
