Research Article
Artificial Neural Network Approach for Estimating Operating Parameters for Predictive Maintenance of Hydraulic Circuit
@INPROCEEDINGS{10.1007/978-3-031-65123-6_29, author={Ivan Kuric and Daria Fedorova and Ivan Zajačko and Vladim\^{\i}r Tlach and Vladim\^{\i}r Stenchl\^{a}k and Andrej Bencel}, title={Artificial Neural Network Approach for Estimating Operating Parameters for Predictive Maintenance of Hydraulic Circuit}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 19th EAI International Conference, QShine 2023, Shenzhen, China, October 8 -- 9, 2023, Proceedings, Part II}, proceedings_a={QSHINE PART 2}, year={2024}, month={8}, keywords={feedforward neural network predictive maintenance correlation surfaces}, doi={10.1007/978-3-031-65123-6_29} }- Ivan Kuric
Daria Fedorova
Ivan Zajačko
Vladimír Tlach
Vladimír Stenchlák
Andrej Bencel
Year: 2024
Artificial Neural Network Approach for Estimating Operating Parameters for Predictive Maintenance of Hydraulic Circuit
QSHINE PART 2
Springer
DOI: 10.1007/978-3-031-65123-6_29
Abstract
This paper deals with the problem of model interpretability of neural network black box models in the terms of predictive maintenance. A testing device for the evaluation of predictive AI models has been presented. The experiment consisted in testing a feedforward neural network model, which was designed for the approximation of a complex multiparametric function and prediction of the monitored parameter (temperature of the working fluid in the tank). Favorable results were obtained for predicting the value of temperature in the working fluid tank based on the other simulation parameters and the simulation run time. Verification of the reliability of the prediction was carried out by additional neural network testing on new data. Correlation surface plots were also plotted and analyzed for the extracted dependencies between the parameters used for the prediction of the parameter of interest using the neural network. The achieved results have perspectives for processing the results obtained by neural network prediction in the field of predictive analytics and maintenance.
