A Machine Learning Approach for Gearbox System Fault Diagnosis
Typ dokumentu
článek v časopisejournal article
Peer-reviewed
publishedVersion
Autor
Vrba J.
Cejnek M.
Steinbach J.
Krbcova Z.
Práva
Creative Commons Attribution (CC BY) 4.0http://creativecommons.org/licenses/by/4.0/
openAccess
Metadata
Zobrazit celý záznamAbstrakt
This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter's prediction error. The obtained prediction error's standard deviation is further processed with a support-vector machine to classify the gearbox's condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings.
Kolekce
- Publikační činnost ČVUT [1312]
Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je Creative Commons Attribution (CC BY) 4.0