Why Machine Fault Diagnosis Should Not Be Treated as an "Overfitting Contest"
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F26%3A0198698" target="_blank" >RIV/00216305:26210/26:0198698 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Why Machine Fault Diagnosis Should Not Be Treated as an "Overfitting Contest"
Original language description
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.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
20301 - Mechanical engineering
Result continuities
Project
<a href="/en/project/EH23_020%2F0008528" target="_blank" >EH23_020/0008528: Innovative Technologies for Smart Low Emission Mobilities</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů