Predicting Safety Logic Device Solutions via Decision Trees and Rules Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU137880" target="_blank" >RIV/00216305:26220/20:PU137880 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9257284" target="_blank" >https://ieeexplore.ieee.org/document/9257284</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCC49264.2020.9257284" target="_blank" >10.1109/ICCC49264.2020.9257284</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Safety Logic Device Solutions via Decision Trees and Rules Algorithms

Popis výsledku v původním jazyce

Considering the extensive data sets and statistical techniques, a digital factory (plant) embodies a branch of machine learning that has an impact on machine safety. We propose a study based on an application of decision trees and rules algorithms (JRIP, J48, Random Forest, Random Tree, and PART). Our experimental data were collected from various industrial machine safety solutions. Diverse validation techniques were employed to derive the classification performance of each method; the approach is expected to simplify the user choice of a suitable safety logic device type. In this study, the overall classification methods proportion of individual safety logic device solutions were correctly assigned by using the training-evaluated test mode, and the prediction accuracy reached 100%; further, when assessing the 5-fold cross-validation test mode, we obtained the success rate of 82% (JRIP and PART). PART as the best method was correctly assigned for the 10-fold cross-validation test mode (85%). New developments within the broad province of machine learning, including the concepts characterized in our study, may facilitate effective assessment of machine safety systems.

Název v anglickém jazyce

Predicting Safety Logic Device Solutions via Decision Trees and Rules Algorithms

Popis výsledku anglicky

Considering the extensive data sets and statistical techniques, a digital factory (plant) embodies a branch of machine learning that has an impact on machine safety. We propose a study based on an application of decision trees and rules algorithms (JRIP, J48, Random Forest, Random Tree, and PART). Our experimental data were collected from various industrial machine safety solutions. Diverse validation techniques were employed to derive the classification performance of each method; the approach is expected to simplify the user choice of a suitable safety logic device type. In this study, the overall classification methods proportion of individual safety logic device solutions were correctly assigned by using the training-evaluated test mode, and the prediction accuracy reached 100%; further, when assessing the 5-fold cross-validation test mode, we obtained the success rate of 82% (JRIP and PART). PART as the best method was correctly assigned for the 10-fold cross-validation test mode (85%). New developments within the broad province of machine learning, including the concepts characterized in our study, may facilitate effective assessment of machine safety systems.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2020 21st International Carpathian Control Conference (ICCC)

ISBN

978-1-7281-1951-9

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IEEE

Místo vydání

High Tatras, Slovakia

Místo konání akce

High Tatras

Datum konání akce

27. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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