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Application of artificial neural networks in condition based predictive maintenance

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F16%3A50005029" target="_blank" >RIV/62690094:18450/16:50005029 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-319-31277-4_7" target="_blank" >http://dx.doi.org/10.1007/978-3-319-31277-4_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>

Alternative languages

  • 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

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Recent developments in intelligent information and database systems

  • ISBN

    978-3-319-31276-7

  • ISSN

    1860-949X

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    75-86

  • Publisher name

    Springer

  • Place of publication

    Berlin

  • Event location

    Da Nang, Vietnam

  • Event date

    Mar 14, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000390824900007