Predictive Maintenance and Intelligent Sensors in Smart Factory: Review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F21%3A43902754" target="_blank" >RIV/60076658:12510/21:43902754 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/1424-8220/21/4/1470" target="_blank" >https://www.mdpi.com/1424-8220/21/4/1470</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s21041470" target="_blank" >10.3390/s21041470</a>
Alternative languages
Result language
angličtina
Original language name
Predictive Maintenance and Intelligent Sensors in Smart Factory: Review
Original language description
With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems' decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper's main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.
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
50204 - Business and management
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sensors
ISSN
1424-8220
e-ISSN
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Volume of the periodical
21
Issue of the periodical within the volume
4
Country of publishing house
CH - SWITZERLAND
Number of pages
40
Pages from-to
"neuvedeno"
UT code for WoS article
000624651800001
EID of the result in the Scopus database
2-s2.0-85100943582