Szczegóły publikacji

Opis bibliograficzny

Advanced domain adaptation for fault diagnosis in condition monitoring / Paweł KNAP, Ireneusz DOMINIK // W: MMAR 2025 [Dokument elektroniczny] : 29th international conference on Methods and Models in Automation and Robotics : 26–29 August 2025, Międzyzdroje, Poland : technical papers : on line proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2025. — ( International Conference on Methods and Models in Automation and Robotics ; ISSN  2835-2815 ). — USB ISBN: 979-8-3315-2648-1. — Print on Demand(PoD) ISBN: 979-8-3315-2650-4. — e-ISBN: 979-8-3315-2649-8. — S. 53–58. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 57–58, Abstr.

Autorzy (2)

Słowa kluczowe

MMDCWRUdeep learningfault diagnosisvibration analysisdomain adaptationcondition monitoringCORAL

Dane bibliometryczne

ID BaDAP162268
Data dodania do BaDAP2025-09-12
Tekst źródłowyURL
DOI10.1109/MMAR65820.2025.11150893
Rok publikacji2025
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
WydawcaInstitute of Electrical and Electronics Engineers (IEEE)
KonferencjaInternational Conference on Methods and Models in Automation and Robotics 2025
Czasopismo/seriaInternational Conference on Methods and Models in Automation and Robotics

Abstract

This paper presents an advanced approach to fault diagnosis and condition monitoring by integrating domain adaptation techniques into a Bayesian-optimized feature extraction framework. Specifically, we enhance model generalization by incorporating Maximum Mean Discrepancy (MMD) and CORrelation ALignment (CORAL) loss functions, which align feature distributions between source and target domains. Experiments conducted on the widely used Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed methods outperform a baseline model trained solely on source data, achieving higher accuracy and robustness under varying operational conditions. These results underscore the potential of advanced domain adaptation strategies for reliable predictive maintenance applications.

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