Predictive maintenance with digital model

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0201546" target="_blank" >RIV/00216305:26220/26:0201546 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Predictive maintenance with digital model
Original language description
This paper addresses the development of a Predictive Maintenance (PdM) detection algorithm applied to a digital model intended for PdM purposes of a heat exchanger station. The detection algorithm has been designed utilising data from a PLC measurement programme in conjunction with a digital model developed using MATLAB Simulink. Machine Learning (ML) techniques, specifically Support Vector Machines (SVM), were employed like two class classificator to identify anomalies. The SVM algorithm classified the measurement points into fault and normal operating states based on modelled temperature values, the Root Mean Square Error (RMSE) of temperatures within the primary circuit. The normal operating states is defined by digital model introduced in [4]. Anomaly state is simulated by serial clogging valve V3 in primary circuit.
Czech name
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Czech description
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Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings I of the 31st Conference STUDENT EEICT 2025: General papers
ISBN
978-80-214-6321-9
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
87-90
Publisher name
VUT
Place of publication
Brno
Event location
Brno
Event date
Apr 29, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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