Predicting Safety Solutions via an Artificial Neural Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU134098" target="_blank" >RIV/00216305:26220/19:PU134098 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2405896319326606?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405896319326606?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ifacol.2019.12.711" target="_blank" >10.1016/j.ifacol.2019.12.711</a>
Alternative languages
Result language
angličtina
Original language name
Predicting Safety Solutions via an Artificial Neural Network
Original language description
Considering the extensive data sets and statistical techniques, Industry 4.0 embodies a branch of machine learning that has a constantly increasing impact on machine safety. We propose an preliminary study based on application of multi-layer feed-forward neural networks in machine safety solutions; the approach is expected to simplify the user choice of suitable measures and safety functions. The prediction method and factors influencing the success rate of the procedure are indicated in a safety parameter scale reflecting industrial experience with classic methods. The multilayer perceptron, a mainstream classification algorithm from the WEKA machine learning workbench, was employed in our primary dataset as a class of the feed-forward artificial neural network. Our initial experimental data were collected from various experts within the industry. The overall proportion of individual safety solutions was correctly assigned by using the training-evaluated test mode, and its prediction accuracy was 100%; further, when assessing the 5-fold cross-validation test mode, we obtained the success rate of 40%. These statistical tools could be used to assess safety PLC traceability systems, and they exhibit the potential to assist managers in decision-making as safety devices. We demonstrate that machine learning is widely usable by the expert community and might bring multiple advantages, such as reduction of the safety solution design time, major cost cutback, and engineering tool availability.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
16th IFAC Conference on Programmable Devices and Embedded Systems PDeS 2019
ISBN
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ISSN
2405-8963
e-ISSN
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Number of pages
6
Pages from-to
490-495
Publisher name
IFAC-PapersOnLine
Place of publication
neuveden
Event location
Tatranská lomnica
Event date
Oct 29, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000507495200082