Detail publikace

Why Machine Fault Diagnosis Should Not Be Treated as an "Overfitting Contest"

REKEM, J. PROKOP, A. OTIPKA, V. KOPEČEK, P.

Anglický název

Why Machine Fault Diagnosis Should Not Be Treated as an "Overfitting Contest"

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

en

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

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.

Klíčová slova anglicky

bearing fault detection; CWRU dataset

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

Číslo edice

1

Strany od–do

192–201

Počet stran

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",
  month="September",
  pages="192--201",
  publisher="Mendel University in Brno, Faculty of AgriSciences",
  address="Brno",
  isbn="978-80-7701-051-1"
}