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Detail publikačního výsledku
REKEM, J.; PROKOP, A.; OTIPKA, V.; KOPEČEK, P.
Originální název
Why Machine Fault Diagnosis Should Not Be Treated as an "Overfitting Contest"
Anglický název
Druh
Stať ve sborníku mimo WoS a Scopus
Originální abstrakt
Machine learning and deep learning algorithms are increasingly popular for machine fault diagnosis tasks. Several freely available datasets serve as a benchmark to compare different diagnostic methods. This article explores the CWRU bearing fault dataset and argues that it is essential to understand the machine's operation principles and dataset properties to develop algorithms capable of transferring from laboratory environment to real-world applications.
Anglický abstrakt
Klíčová slova
bearing fault detection; CWRU dataset
Klíčová slova v angličtině
Autoři
Vydáno
04.09.2025
Nakladatel
Mendel University in Brno, Faculty of AgriSciences
Místo
Brno
ISBN
978-80-7701-051-1
Kniha
56th INTERNATIONAL SCIENTIFIC CONFERENCE FOCUSED ON RESEARCH AND TEACHING METHODS RELATED TO VEICLES AND DRIVES
Strany od
192
Strany do
201
Strany počet
10
BibTex
@inproceedings{BUT198698, author="Jakub {Rekem} and Aleš {Prokop} and Václav {Otipka} and Pavel {Kopeček}", title="Why Machine Fault Diagnosis Should Not Be Treated as an {"}Overfitting Contest{"}", booktitle="56th INTERNATIONAL SCIENTIFIC CONFERENCE FOCUSED ON RESEARCH AND TEACHING METHODS RELATED TO VEICLES AND DRIVES", year="2025", number="1", pages="192--201", publisher="Mendel University in Brno, Faculty of AgriSciences", address="Brno", isbn="978-80-7701-051-1" }