Processing signal information from multisensor system in tasks of monitoring the quality of objects

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Abstract

A method and algorithm for processing signal information from multisensor system in tasks of monitoring the quality of objects was proposed. The developed method was tested on a data set obtained during an experiment using an array of potentiometric sensors on real industrial samples of the analyzed objects. Identification quality indicators were compared with those previously known in the world scientific literature. As a result of applying the developed approach, an increase in the precision of the analysis is observed due to the usage in the monitoring system of time series values for previous points in time and applying of weighting coefficients for the significance of measurement results. The described approach can be used at "Industry 4.0" enterprises in software that provides quality monitoring of production processes, including in real time, as well as for processing data from a multisensor system during express analysis of samples.

About the authors

V. V. Semenov

St. Petersburg Federal Research Center of the Russian Academy of Sciences

Author for correspondence.
Email: v.semenov@spcras.ru

Ph.D., Senior Researcher

Russian Federation, St. Petersburg

References

  1. Caruana L., Francalanza E. А Safety 4.0 Approach for Collaborative Robotics in the Factories of the Future, Procedia Computer Science, 2023, vol. 217, pp. 17847—1793, doi: 10.1016/j.procs.2022.12.378.
  2. Yu X., Fu L., Wang T., Liu Z., Niu N., Chen L. Multivariate chemical analysis: From sensors to sensor arrays, Chinese Chemical Letters, 2024, vol. 35 (7), article num. 109167, doi: 10.1016/j.cclet.2023.109167.
  3. Nam S.-H., Lee J., Kim E., Koo J.-W., Shin Y., Hwang T.-M. Electronic tongue for the simple and rapid determination of taste and odor compounds in water, Chemosphere, 2023, vol. 338, article num. 139511, doi: 10.1016/j.chemosphere.2023.139511.
  4. Zhang X., Wang T., Ni W., Zhang Y., Lv W., Zeng M., Yang J., Hu N., Zhan R., Li G., Hong Z., Yang Z. Sensor array optimization for the electronic nose via different deep learning methods, Sensors and Actuators В: Chemical, 2024, vol. 410, article num. 135579, doi: 10.1016/j.snb.2024.135579.
  5. Yuan S., Yang M., Reniers G. Integrated process safety and process security risk assessment of industrial cyber-physical systems in chemical plants, Computers in Industry, 2024, vol. 155, article num. 104056, doi: 10.1016/j.compind.2023.104056.
  6. Wang J., Du W., Lei Y., Chen Y., Wang Z., Mao K., Tao S., Pan В. Quantifying the dynamic characteristics of indoor air pollution using real-time sensors: Current status and future implication, Environment International, 2023, vol. 175, article num. 107934, doi: 10.1016/j.envint.2023.107934.
  7. Semenov V. V. The Method of Forming Informative Features in Tasks of Quantitative Analysis of Objects, Information technologies, 2023, vol. 29, no. 9, pp. 467—472, doi: 10.17587/it.29.467-472 (in Russian).
  8. Semenov V. V. Method for monitoring the state of elements of cyber-physical systems based on time series analysis, Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 6, pp. 1150—1158, doi: 10.17586/2226-1494-2022-22-6-1150-1158 (in Russian).
  9. Semenov V., Salakhutdinova K., Lebedev I., Sukhoparov M. Identification of abnormal functioning during the operation devices of cyber-physical systems, Journal of Applied Informatics, 2019, vol. 14, no. 6 (84), pp. 114—122, doi: 10.24411/1993-8314-2019-10053(in Russian).
  10. Loreti D., Visani G. Parallel approaches for a decision tree-based explainability algorithm, Future Generation Computer Systems, 2024, vol. 158, pp. 308—322, doi: 10.1016/j.future.2024.04.044.
  11. Yuan S., Yang M., Reniers G. Integrated process safety and process security risk assessment of industrial cyber-physical systems in chemical plants, Computers in Industry, 2024, vol. 155, Article num. 104056, doi: 10.1016/j.compind.2023.104056.
  12. Vlasov Yu. G., Bychkov E. A., Legin А. V. Chalcogenide glass chemical sensors: Research and analytical applications, Talanta, 1994, vol. 41 (6), pp. 1059—1063, doi: 10.1016/0039-9140(94)00124-3.
  13. Semenov V., Volkov S., Khaydukova M., Fedorov A., Lisitsyna I., Kirsanov D., Legin А. Determination of three quality parameters in vegetable oils using potentiometric e-tongue, Journal of Food Composition and Analysis, 2019, vol. 75, pp. 75—80, doi: 10.1016/j.jfca.2018.09.015.
  14. Khan N., Ali S. Multi-sensor random sample consensus for instantaneous frequency estimation of multi-component signals, Digital Signal Processing, 2023, vol. 140, article num. 104129, doi: 10.1016/j.dsp.2023.104129.
  15. Khan S., Nath T., Hossain M., Mukherjee A., Hasnath H., Meem T., Khan U. Comparison of multiclass classification techniques using dry bean dataset, International Journal of Cognitive Computing in Engineering, 2023, vol. 4, pp. 6—20, doi: 10.1016/j.ijcce.2023.01.002.

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