Investigation of combined ensemble methods for diagnostics of the quality of interaction of human-machine systems

User Rating:  / 1
PoorBest 

Authors:


Oleksandr Laktionov*, orcid.org/0000-0002-5230-524X, National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Leonid Lievi, orcid.org/0000-0003-4619-8764, National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Andrii Tretiak, orcid.org/0000-0003-3971-3078, National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Mykola Movin, orcid.org/0000-0002-7470-6482, National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail This email address is being protected from spambots. You need JavaScript enabled to view it.


повний текст / full article



Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2023, (4): 138 - 143

https://doi.org/10.33271/nvngu/2023-4/138



Abstract:



Purpose.
Study on the process of combining several methods for determining the quality indices of human-machine interaction, containing various configurations for determining the weight coefficients in an ensemble.


Methodology.
The process of diagnosing the quality of the interaction of a human-machine system with four elements of subsystems is studied using the example of the system “Operator–Machining Center – Control Program – Safe Environment”. The main hypothesis of the study is the combination of several methods for determining the quality indices of human-machine interaction, containing different configurations for determining the weight coefficients in the ensemble. A combined method for diagnosing the quality of interaction between human-machine systems based on ensemble models, which include non-ensemble ones, has been proposed. The ensemble index has been determined by averaging the non-ensemble indices. The defined ensemble indices and element scores of the four subsystems are used as input scores to a multiple regression model to generate prediction.


Findings.
Four combinations of ensemble indices have been developed and implemented in software, which are characterized by a minimum value of the standard deviation compared to the existing ones. According to the results of experimental verification, the proposed models demonstrate the value of the standard deviation of 0.1404; 0.1401; 0.1411; 0.1397, and the existing ones are 0.1532; 0.1535; 0.1532; 0.1532.


Originality.
The combined ensemble method for diagnosing the quality of interaction between elements of subsystems takes into account linear models with non-linear variables and different ways of determining weight coefficients.


Practical value.
The scenario for the practical use of the results obtained is a possible option for optimizing production, where, depending on the final result, specialists can adjust the value of a particular subsystem to achieve the desired result.



Keywords:
ensemble index, averaging model, nonlinear relationship, linear equation, nonlinear transformation, IT-technologies

References.


1. Zhang, Y., Liu, W., Shi, Q., Huang, Y., & Huang, S. (2022). Resilience assessment of multi-decision complex energy interconnection system. International Journal of Electrical Power & Energy Systems137, 107809. https://doi.org/10.1016/j.ijepes.2021.107809.

2. Komelina, O., Dubishchev, V., Lysenko, M., & Panasenko, N. (2018). Ukraine Unified Transport System Potential and Its Development Management Effectiveness Integral Assessment. International Journal of Engineering & Technology7(4.3), 633. https://doi.org/10.14419/ijet.v7i4.3.19972.

3. Belitz, K., & Stackelberg, P. E. (2021). Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression models. Environmental Modelling & Software, 139, 105006. https://doi.org/10.1016/j.envsoft.2021.105006.

4. Yu, C., Zhu, Y.-P., Luo, H., Luo, Z., & Li, L. (2023). Design assessments of complex systems based on design oriented modelling and uncertainty analysis. Mechanical Systems and Signal Processing188, 109988. https://doi.org/10.1016/j.ymssp.2022.109988.

5. Zhang, K., Shao, X., Chen, Z., Liu, X., & Wang, W. (2021). A multi-level diagnostic method for a human-machine system based on the fusion of ELM and the D-S evidence theory. Information Fusion, 22. https://doi.org/10.3390/s22155902.

6. Jiao, J., Zhao, M., Lin, J., & Liang, K. (2020). A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 417, 36-63. https://doi.org/10.1016/j.neucom.2020.07.088.

7. Justus, V., & Kanagachidambaresan, G. R. (2022). Intelligent Single-Board Computer for Industry 4.0: Efficient Real-Time Monitoring System for Anomaly Detection in CNC Machines. Microprocessors and Microsystems, 104629. https://doi.org/10.1016/j.micpro.2022.104629.

8. Ghasemi, A., Ashoori, A., & Heavey, C. (2021). Evolutionary Learning Based Simulation Optimization for Stochastic Job Shop Scheduling Problems. Applied Soft Computing106, 107309. https://doi.org/10.1016/j.asoc.2021.107309.

9. López Martínez, P., Dintén, R., Drake, J. M., & Zorrilla, M. (2021). A big data-centric architecture metamodel for Industry 4.0. Future Generation Computer Systems, 125, 263-284. https://doi.org/10.1016/j.future.2021.06.020.

10. Yang, B., Qiao, L., Zhu, Z., & Wulan, M. (2016). A Metamodel for the Manufacturing Process Information Modeling. Procedia CIRP56, 332-337. https://doi.org/10.1016/j.procir.2016.10.032.

11. Dunke, F., & Nickel, S. (2020). Neural networks for the metamodeling of simulation models with online decision making. Simulation Modelling Practice and Theory, 99, 102016. https://doi.org/10.1016/j.simpat.2019.102016.

12. Chattopadhyay, A., Nabizadeh, E., Bach, E., & Hassanzadeh, P. (2023). Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems. Journal of Computational Physics, 445, 110613. https://doi.org/10.1016/j.jcp.2023.111918.

13. Ochella, S., Shafiee, M., & Dinmohammadi, F. (2022). Artificial intelligence in prognostics and health management of engineering systems. Engineering Applications of Artificial Intelligence108, 104552. https://doi.org/10.1016/j.engappai.2021.104552.

14. De Souza, I. T., Carolina Rosa, A., Patriarca, R., & Haddad, A. (2022). Soft computing for nonlinear risk assessment of complex socio-technical systems. Expert Systems with Applications, 117828. https://doi.org/10.1016/j.eswa.2022.117828.

15. Singh, M., & Chauhan, S. (2023). A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems. Electric Power Systems Research214, 108923. https://doi.org/10.1016/j.epsr.2022.108923.

16. Nguyen, P. T., Di Ruscio, D., Pierantonio, A., Di Rocco, J., & Iovino, L. (2021). Convolutional neural networks for enhanced classification mechanisms of metamodels. Journal of Systems and Software172, 110860. https://doi.org/10.1016/j.jss.2020.110860.

17. Krouska, A., Troussas, C., & Sgouropoulou, C. (2019). Fuzzy Logic for Refining the Evaluation of Learners’ Performance in Online Engineering Education. European Journal of Engineering Research and Science, 4(6), 50-56.

18. Laktionov, О. (2021). Improvement of methods for determination of quality indices of interaction elements of system subsystems. Eastern-European Journal of Enterprise Technologies, 6(3(114)), 72-82. https://doi.org/10.15587/1729-4061.2021.244929.

 

Visitors

6697797
Today
This Month
All days
841
201985
6697797

Guest Book

If you have questions, comments or suggestions, you can write them in our "Guest Book"

Registration data

ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

Contacts

D.Yavornytskyi ave.,19, pavilion 3, room 24-а, Dnipro, 49005
Tel.: +38 (056) 746 32 79.
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
You are here: Home Authors and readers EngCat Archive 2023 Content №4 2023 Investigation of combined ensemble methods for diagnostics of the quality of interaction of human-machine systems