A roadmap to fault diagnosis of industrial machines via machine learning: A brief review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10256521" target="_blank" >RIV/61989100:27230/25:10256521 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/abs/pii/S0263224124021018" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0263224124021018</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.measurement.2024.116216" target="_blank" >10.1016/j.measurement.2024.116216</a>
Alternative languages
Result language
angličtina
Original language name
A roadmap to fault diagnosis of industrial machines via machine learning: A brief review
Original language description
In fault diagnosis, machine learning theories are gaining popularity as they proved to be an efficient tool that not only reduces human effort but also identifies the health conditions of the machines automatically. In this work, an attempt has been made to systematically review the progress of machine learning theories in fault diagnosis from scratch to future perspectives. Initially, artificial intelligence came into the picture which started to weaken the human effort whose efficiency relies on feature extraction which depends on expert knowledge. The introduction of deep learning theories has reformed the fault diagnosis process by realising the artificial aid, encouraging end-to-end encryption in the diagnostic procedure. The deep learning theories have also filled the gap between the large amount of monitoring data and the health conditions of industrial machines. The future of deep learning theories i.e. transfer learning which uses the knowledge of one domain to another related domain during fault diagnosis has been reviewed. In last, the research trends of the machine learning theories have been briefly discussed along with their challenges in fault diagnostics. (C) 2024 Elsevier Ltd
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20301 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Measurement
ISSN
0263-2241
e-ISSN
1873-412X
Volume of the periodical
242
Issue of the periodical within the volume
116216
Country of publishing house
US - UNITED STATES
Number of pages
28
Pages from-to
nestránkováno
UT code for WoS article
001359921300001
EID of the result in the Scopus database
2-s2.0-85209254329