Features of the average bihoherentity of the dynamics of the parameters of the gas environment at the appearance of fire

 

Pospelov Boris

National University of Civil Defenсe of Ukraine

http://orcid.org/0000-0002-0957-3839

 

Rybka Evgeniy

National University of Civil Defenсe of Ukraine

http://orcid.org/0000-0002-5396-5151

 

Meleshchenko Ruslan

National University of Civil Defenсe of Ukraine

http://orcid.org/0000-0001-5411-2030

 

Bezuhla Yuliiа

National University of Civil Defenсe of Ukraine

http://orcid.org/0000-0003-4022-2807

 

Yashchenko Oleksandr

National University of Civil Defenсe of Ukraine

http://orcid.org/0000-0001-7129-389X

 

Borodych Pavlo

National University of Civil Defenсe of Ukraine

http://orcid.org/0000-0001-9933-8498

 

DOI: https://doi.org/10.52363/2524-0226-2024-39-16

 

Keywords: measure, average bicoherence, change of dangerous parameters, gas medium, ignition of material

 

Аnnotation

 

The object of the study is the dangerous parameters of the gas environment during the ignition of materials in the premises. The importance of such research is related to the possibility of using the average bicoherence measure to detect fires and prevent emergency situations due to fire. The measure of the average bicoherence of arbitrary dangerous parameters of the gas environment on a free time interval is substantiated. Experimentally studied features of the average bicoherence measure of the frequency components of the spectrum of the main dangerous parameters of the gas environment in the model chamber at the intervals of reliable absence and presence of ignition of typical ignition materials. The results show that the dynamics of the dangerous parameters of the gas environment in the chamber at the intervals of the absence and presence of fires has a complex non-linear nature. It was established that the difference in the average bicoherence measure for the frequency components in the spectrum of changes in the hazardous parameters of the gas environment in the presence and absence of fires has a different and individual character. It is noted that the individual characteristics of the average bicoherence measure can act as a possible sign for the detection of fires. It was found that the maximum value of the measure equal to 1.0 for all frequency indices occurs before the ignition of alcohol and paper. Before the ignition of wood and textiles, the values of the frequency index measures are random and range from 0.4 to 0.8. It was established that the ignition of materials leads to the loss of the initial third-order relationships between the frequency components in the spectra. This makes it possible to consider the specified loss of connections as a general sign of fire detection by calculating the proposed measure of average bicoherence of hazardous parameters of the gas environment in the premises.

 

References

 

  1. Vambol, S., Vambol, V., Sychikova, Y., Deyneko, N. (2017). Analysis of the ways to provide ecological safety for the products of nanotechnologies throughout their life cycle. Eastern-European Journal of Enterprise Technologies, 1(10(85)), 27–36. doi: 10.15587/1729-4061.2017.85847
  2. Semko, A., Rusanova, O., Kazak, O., Beskrovnaya, M., Vinogradov, S., Gricina, I. (2015). The use of pulsed high-speed liquid jet for putting out gas blow-out. The International Journal of Multiphysics, 9(1), 9–20. doi: 10.1260/1750-9548.9.1.9
  3. Popov, O., Іatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V. et al. (2018). Conceptual Approaches for Development of Informational and Analytical Expert System for Assessing the NPP impact on the Environment. Nuclear and Radiation Safety, 3(79), 56–65. doi: 10.32918/nrs.2018.3(79).09
  4. Pospelov, B., Andronov, V., Rybka, E., Popov, V., Semkiv, O. (2018). Devel-opment of the method of frequencytemporal representation of fluctuations of gaseous medium parameters at fire. Eastern-European Journal of Enterprise Technologies, 2(10(92)), 44–49. doi: 10.15587/1729-4061.2018.125926
  5. Dubinin, D., Korytchenko, K., Lisnyak, A., Hrytsyna, I., Trigub, V. (2017). Numerical simulation of the creation of a fire fighting barrier using an explosion of a combustible charge. Eastern-European Journal of Enterprise Technologies, 6(10(90)), 11–16. doi: 10.15587/1729-4061.2017.114504
  6. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V., Sokolov, D., Dement, M., Hurkovskyi, V., Nikolaiev, K., Yatsyshyn T., Dimitriieva, D. (2019). Physical features of pollutants spread in the air during the emergency at NPPs. Nuclear and Radiation Safety, 4/84, 11. doi: 10.32918/nrs.2019.4(84).11
  7. Vambol, V., Vambol, S., Kondratenko, O., Koloskov, V., Suchikova, Y. (2018). Substantiation of expedience of application of high-temperature utilization of used tires for liquefied methane production. Journal of Achievements in Materials and Manufacturing Engineering, 87(2), 77–84. doi: 10.5604/01.3001.0012.2830
  8. Dubinin, D., Korytchenko, K., Lisnyak, A., Hrytsyna, I., Trigub, V. (2018). Improving the installation for fire extinguishing with finelydispersed water. Eastern-European Journal of Enterprise Technologies, 2(10(92)), 38–43. doi: 10.15587/1729-4061.2018.127865
  9. Otrosh, Y., Rybka, Y., Danilin, O., Zhuravskyi, M. (2019). Assessment of the technical state and the possibility of its control for the further safe operation of building structures of mining facilities. E3S Web of Conferences, 123, 01012. doi: 10.1051/e3sconf/201912301012
  10. Ragimov, S., Sobyna, V., Vambol, S., Vambol, V., Feshchenko, A., Zakora, A., Strejekurov, E., Shalomov, V. (2018). Physical modelling of changes in the energy impact on a worker taking into account high-temperature radiation. Journal of Achievements in Materials and Manufacturing Engineering, 91, 1, 27–33. doi: 10.5604/01.3001.0012.9654
  11. Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Maksymenko, N., Meleshchenko, R. et al. (2020). Mathematical model of determining a risk to the human health along with the detection of hazardous states of urban atmosphere pollution based on measuring the current concentrations of pollutants. Eastern-European Journal of Enterprise Technologies, 4(10(106)), 37–44. doi: 10.15587/1729-4061.2020.210059
  12. Otrosh, Yu., Semkiv, O., Rybka, E., Kovalov, A. (2019). About need of calcu-lations for the steel framework building in temperature influences conditions. IOP Conference Series: Materials Science and Engineering, 708, 1, 012065. doi: 10.1088/1757-899x/708/1/012065
  13. Vambol, S., Vambol, V., Kondratenko, O., Suchikova, Y., Hurenko, O. (2017). Assessment of improvement of ecological safety of power plants by arranging the system of pollutant neutralization. Eastern-European Journal of Enterprise Technol-ogies, 3/10(87), 63–73. doi: 10.15587/1729-4061.2017.102314
  14. Rybalova, O., Artemiev, S., Sarapina, M., Tsymbal, B., Bakharevа, A., Shestopalov, O., Filenko, O. (2018). Development of methods for estimating the envi-ronmental risk of degradation of the surface water state. Eastern-European Journal of Enterprise Technologies, 2(10(92)), 4–17. doi: 10.15587/1729-4061.2018.127829
  15. World Fire Statistics. (2022). № 27. CTIF, 65. Available at: https://www.ctif.org/sites/default/files/2022-08/CTIF_Report27_ESG.pdf
  16. Kovalov, A., Otrosh, Y., Rybka, E., Kovalevska, T., Togobytska, V., Rolin, I. (2020). Treatment of Determination Method for Strength Characteristics of Reinforcing Steel by Using Thread Cutting Method after Temperature Influence. Materials Science Forum, 1006, 179–184. doi: 10.4028/www.scientific.net/msf.1006.179
  17. Pospelov, B., Andronov, V., Rybka, E., Samoilov, M., Krainiukov, O., Biryukov, I., Butenko, T., Bezuhla, Yu., Karpets, K., Kochanov, E. (2021). Develop-ment of the method of operational forecasting of fire in the premises of objects under real conditions. Eastern-European Journal of Enterprise Technologies, 2/10(110), 43–50. doi: 10.15587/1729-4061.2021.226692
  18. Andronov, V., Pospelov, B., Rybka, E. (2017). Development of a method to improve the performance speed of maximal fire detectors. Eastern-European Journal of Enterprise Technologies, 2(9(86)), 32–37. doi: 10.15587/1729-4061.2017.96694
  19. Pospelov, B., Andronov, V., Rybka, E., Skliarov, S. (2017). Research into dy-namics of setting the threshold and a probability of ignition detection by self­adjusting fire detectors. Eastern-European Journal of Enterprise Technologies, 5/9(89), 43–48. doi: 10.15587/1729-4061.2017.110092
  20. Cheng, C., Sun, F., Zhou, X. (2011). One fire detection method using neural networks. Tsinghua Science and Technology, 16(1), 31–35. doi: 10.1016/s1007-0214(11)70005-0
  21. Ding, Q., Peng, Z., Liu, T., Tong, Q. (2014). Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence Theory. Algo-rithms, 7(4), 523–537. doi: 10.3390/a7040523
  22. Wu, Y., Harada, T. (2004). Study on the Burning Behaviour of Plantation Wood. Scientia Silvae Sinicae, 40, 131–136.
  23. Ji, J., Yang, L., Fan, W. (2003). Experimental Study on Effects of Burning Behaviours of Materials Caused by External Heat Radiation. Journal of Combustion Science and Technology, 9, 139.
  24. Peng, X., Liu, S., Lu, G. (2005). Experimental Analysis on Heat Release Rate of Materials. Journal of Chongqing University, 28, 122.
  25. Pospelov, B., Andronov, V., Rybka, E., Meleshchenko, R., Gornostal, S. (2018). Analysis of correlation dimensionality of the state of a gas medium at early ignition of materials. Eastern-European Journal of Enterprise Technologies, 5/10(95), 25–30. doi: 10.15587/1729-4061.2018.142995
  26. Pospelov, B., Andronov, V., Rybka, E., Meleshchenko, R., Borodych, P. (2018). Studying the recurrent diagrams of carbon monoxide concentration at early ignitions in premises. Eastern-European Journal of Enterprise, 3/9(93), 34–40. doi: 10.15587/1729-4061.2018.133127
  27. Pospelov, B., Rybka, E., Meleshchenko, R., Krainiukov, O., Biryukov, I., Butenko, T., Yashchenko, O., Bezuhla, Yu., Karpets, K., Vasylchenko, R. (2021). Short-term fire forecast based on air state gain recurrency and zero-order Brown model. Eastern-European Journal of Enterprise, 3/10(111), 27–33. doi: 10.15587/1729-4061.2021.233606
  28. Pospelov, B., Rybka, E., Krainiukov, O., Yashchenko, O., Bezuhla, Y., Bielai, S., Kochanov, E., Hryshko, S., Poltavski, E., Nepsha, O. (2021). Short-term forecast of fire in the premises based on modification of the Brown’s zero-order model. Eastern-European Journal of Enterprise Technologies, 4/10(112), 52–58. doi: 10.15587/1729-4061.2021.238555
  29. Pospelov, B., Rybka, E., Togobytska, V., Meleshchenko, R., Danchenko, Yu. (2019). Construction of the method for semi-adaptive threshold scaling transformation when computing recurrent plots. Eastern-European Journal of Enterprise Technologies, 4/10(100), 22–29. doi: 10.15587/1729-4061.2019.176579
  30. Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Karpets, K., Pirohov, O., Semenyshyna, I., Kapitan, R, Promska, A., Horbov, O. (2019). Development of the correlation method for operative detection of recurrent states. Eastern-European Journal of Enterprise, 6/4(102), 39–46. doi: 10.15587/1729-4061.2019.187252
  31. Sadkovyi, V., Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Rud, А., Karpets, K., Bezuhla, Yu. (2020). Construction of a method for detecting arbitrary hazard pollutants in the atmospheric air based on the structural function of the current pollutant concentrations. Eastern-European Journal of Enterprise, 6/10(108), 14–22. doi: 10.15587/1729-4061.2020.218714
  32. Pospelov, B., Rybka, E., Meleshchenko, R., Krainiukov, O., Harbuz, S., Bezuhla, Y. et al. (2020). Use of uncertainty function for identification of hazardous states of atmospheric pollution vector. Eastern-European Journal of Enterprise Tech-nologies, 2(10(104)), 6–12. doi: 10.15587/1729-4061.2020.200140
  33. Floyd, J., Forney, G., Hostikka, S., Korhonen, T., McDermott, R., McGrattan, K. (2013). Fire Dynamics Simulator (Version 6) User’s Guide, 1. National Insti-tute of Standard and Technology.
  34. Polstiankin, R. M., Pospelov, B. B. (2015). Stochastic models of hazardous factors and parameters of a fire in the premises. Problemy pozharnoy bezopasnosti, 38, 130–135. Available at: http://nbuv.gov.ua/UJRN/Ppb_2015_38_24
  35. Heskestad, G., Newman, J. S. (1992). Fire detection using cross-correlations of sensor signals. Fire Safety Journal, 18(4), 355–374. doi: 10.1016/0379-7112(92)90024-7
  36. Gottuk, D. T., Wright, M. T., Wong, J. T., Pham, H. V., Rose-Pehrsson, S. L., Hart, S., Hammond, M., Williams, F. W., Tatem, P. A., Street, T. T. (2002). Prototype Early Warning Fire Detection Systems: Test Series 4 Results. NRL/MR/6180–02–8602, Naval Research Laboratory.
  37. Pospelov, B., Andronov, V., Rybka, E., Bezuhla, Y., Liashevska, O., Butenko, T., Darmofal, E., Hryshko, S., Kozynska, I., Bielashov, Y. (2022). Empirical cumulative distribution function of the characteristic sign of the gas environment during fire. Eastern-European Journal of Enterprise Technologies, 4(10(118)), 60–66. doi: 10.15587/1729-4061.2022.263194
  38. Pospelov, B., Rybka, E., Savchenko, A., Dashkovska, O., Harbuz, S., Naden, E. et al. (2022). Peculiarities of amplitude spectra of the third order for the early detection of indoor fires. Eastern-European Journal of Enterprise Technologies, 5(10(119)), 49–56. doi: 10.15587/1729-4061.2022.265781
  39. Pospelov, B., Bezuhla, Y., Yashchenko, O., Khalmuradov, B., Petukhova, O., Gornostal, S. et al. (2022). Revealing the features of the third order phase spectrum of the main dangerous parameters of the gas medium. Eastern-European Journal of Enter-prise Technologies, 6(10(120)), 63–70. doi: 10.15587/1729-4061.2022.268437
  40. Pasport. Spovishchuvach pozhezhnyi teplovyi tochkovyi. Arton. Available at: https://ua.arton.com.ua/files/passports/%D0%A2%D0%9F%D0%A2-4_UA.pdf
  41. Pasport. Spovishchuvach pozhezhnyi dymovyi tochkovyi optychnyi. Arton. Available at: https://ua.arton.com.ua/files/passports/spd-32_new_pas_ua.pdf
  42. Optical/Heat Multisensor Detector (2019). Discovery. Available at: https://www.nsc-hellas.gr/pdf/APOLLO/discovery/B02704-00%20Discovery%20Multisensor%20Heat-%20Optical.pdf
  43. Saeed, M., Alfatih, S. (2013). Nonlinearity detection in hydraulic machines utilizing bispectral analysis. TJ Mechanical engineering and machinery, 13–21.
  44. Yang, K., Zhang, R., Chen, S., Zhang, F., Yang, J., Zhang, X. (2015). Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis. Algorithms, 8, 929–950. doi: 10.3390/a8040929
  45. Chua, K. C., Chandran, V., Acharya, U. R., Lim, C. M. (2010). Application of higher order statistics/spectra in biomedical signals – Areview. Medical Engineering & Physics, 32(7), 679–689. doi: 10.1016/j.medengphy.2010.04.009
  46. Chua, K. C., Chandran, V., Acharya, U. R., Lim, C. M. (2008). Cardiac state diagnosis using higher order spectra of heart rate variability. Journal of Medical Engi-neering & Technology, 32(2), 145–155. doi: 10.1080/03091900601050862
  47. Cui, L., Xu, H., Ge, J., Cao, M., Xu, Y., Xu, W., Sumarac, D. (2021). Use of Bispectrum Analysis to Inspect the Non-Linear Dynamic Characteristics of Beam-Type Structures Containing a Breathing Crack. Sensors., 21, 1177. doi: 10.3390/s21041177
  48. Martín-Montero, A., Gutiérrez-Tobal, G. C., Kheirandish-Gozal, L., Jiménez-García, J., Álvarez, D., del Campo, F. et al. (2020). Heart rate variability spectrum characteristics in children with sleep apnea. Pediatric Research, 89(7), 1771–1779. doi: 10.1038/s41390-020-01138-2
  49. Max, J. (1981). Principes generaus et methods classiques. Tome 1. Paris New York Barselone Milan Mexico Rio de Janeiro, 311.
  50. Mohankumar, K. (2015). Implementation of an underwater target classifier using higher order spectral features. Cochin. Available at: https://dyuthi.cusat.ac.in/xmlui/bitstream/handle/purl/5368/T-2396.pdf?sequence=1