Application of artificial neural networks in condition based predictive maintenance

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG44__%2F16%3A43875711" target="_blank" >RIV/60162694:G44__/16:43875711 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-31277-4_7" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-31277-4_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-31277-4_7" target="_blank" >10.1007/978-3-319-31277-4_7</a>

Result language
angličtina
Original language name
Application of artificial neural networks in condition based predictive maintenance
Original language description
This paper reviews different techniques of maintenance, artificial neural networks (ANN) and their various applications in fault risk assessment and an early fault detection analysis. The predictive maintenance is in focus of production facilities supplying in long supplier chains of automotive industry to ensure the reliable and continuous production and on-time deliveries. ANN offer a powerful tool to evaluate machine data and parameters which can learn from process data of fault simulation. Finally there are reviewed examples of usage of ANN in specific predictive maintenance cases.
Czech name
—
Czech description
—

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Recent Developments in Intelligent Information and Database Systems
ISBN
978-3-319-31277-4
ISSN
1860-949X
e-ISSN
—
Number of pages
12
Pages from-to
75-86
Publisher name
Springer
Place of publication
Berlin
Event location
Da Nang
Event date
Mar 14, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000390824900007

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