Peculiarities of amplitude spectra of the third order for the early detection of indoor fires

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.265781

Keywords:

materials ignition, gas environment of premises, amplitude bispectrum, dynamic range, detection of fires

Abstract

The object of this study is the dynamics of hazardous parameters of the gas environment during the ignition of materials. The problem that was solved is the early detection of fires in the premises. The research results indicate the nonlinear nature of the dynamics of hazardous parameters of the gas environment in the absence and presence of materials ignition. It was established that the bispectrum amplitude, in contrast to the amplitude spectrum of the hazardous parameters of the gas medium, contains information on the reliable detection of fires. As such information, the value of the positive dynamic amplitude range of bispectrum is used. It was established that during the ignition of alcohol, the positive dynamics of the amplitude bispectrum of all dangerous parameters of the gas medium change. Significant changes are characteristic of smoke density (from 1 dB to 30 dB) and temperature (from 1 dB to 70 dB). The dynamic range of amplitude bispectrum for CO concentration is increased from 30 dB to 70 dB. Paper ignition was found to reduce the dynamic range of the amplitude bispectrum for smoke density from 40 dB to 20 dB. At the same time, the dynamic range of amplitude bispectrum for carbon monoxide concentration and temperature increases to 60 dB. The ignition of wood causes an increase in the dynamic range of the amplitude bispectrum relative to the concentration of carbon monoxide from 40 dB to 60 dB, and the temperature – from 30 dB to 40 dB. It was established that when textiles are ignited, the range of dynamics of the amplitude bispectrum for temperature increases from 10 dB to 60 dB. The results indicate that the dynamic characteristics of the amplitudes of the bispectrum of the gas medium can be used in practice for the early detection of fires in the premises

Author Biographies

Boris Pospelov, Scientific-Methodical Center of Educational Institutions in the Sphere of Civil Defence

Doctor of Technical Sciences, Professor

Evgenіy Rybka, National University of Civil Defence of Ukraine

Doctor of Technical Sciences, Senior Researcher

Research Center

Alexander Savchenko, National University of Civil Defence of Ukraine

PhD, Senior Researcher

Department of Prevention Activities and Monitoring

Olena Dashkovska, Institute of Education Content Modernization

PhD, Associate Professor

Department of Scientific and Methodological Support For Improving the Quality of Education

Serhii Harbuz, National University of Civil Defence of Ukraine

PhD

Department of Prevention Activities and Monitoring

Elena Naden, National University of Civil Defence of Ukraine

PhD, Associate Professor

Department of Prevention Activities and Monitoring

Ivan Chornomaz, Cherkasy Institute of Fire Safety named after Chornobyl Heroes of National University of Civil Protection of Ukraine

PhD

Department of Fire Tactics and Rescue Operations

Svitlana Hryshko, Bogdan Khmelnitsky Melitopol State Pedagogical University

PhD

Department of Physical Geography and Geology

Oleksandr Nepsha, Bogdan Khmelnitsky Melitopol State Pedagogical University

Department of Physical Geography and Geology

Dmytrо Morkvin, National Academy of the National Guard of Ukraine

Research Center

References

  1. Vambol, S., Vambol, V., Bogdanov, I., Suchikova, Y., Rashkevich, N. (2017). Research of the influence of decomposition of wastes of polymers with nano inclusions on the atmosphere. Eastern-European Journal of Enterprise Technologies, 6 (10 (90)), 57–64. doi: https://doi.org/10.15587/1729-4061.2017.118213
  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: https://doi.org/10.1260/1750-9548.9.1.9
  3. Andronov, V., Pospelov, B., Rybka, E., Skliarov, S. (2017). Examining the learning fire detectors under real conditions of application. Eastern-European Journal of Enterprise Technologies, 3 (9 (87)), 53–59. doi: https://doi.org/10.15587/1729-4061.2017.101985
  4. Migalenko, K., Nuianzin, V., Zemlianskyi, A., Dominik, A., Pozdieiev, S. (2018). Development of the technique for restricting the propagation of fire in natural peat ecosystems. Eastern-European Journal of Enterprise Technologies, 1 (10 (91)), 31–37. doi: https://doi.org/10.15587/1729-4061.2018.121727
  5. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V. et. al. (2019). Physical Features of Pollutants Spread in the Air During the Emergency at NPPs. Nuclear and Radiation Safety, 4 (84), 88–98. doi: https://doi.org/10.32918/nrs.2019.4(84).11
  6. Vambol, S., Vambol, V., Sobyna, V., Koloskov, V., Poberezhna, L. (2019). Investigation of the energy efficiency of waste utilization technology, with considering the use of low-temperature separation of the resulting gas mixtures. Energetika, 64 (4). doi: https://doi.org/10.6001/energetika.v64i4.3893
  7. 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: https://doi.org/10.15587/1729-4061.2018.127865
  8. Kovalov, A., Otrosh, Y., Ostroverkh, O., Hrushovinchuk, O., Savchenko, O. (2018). Fire resistance evaluation of reinforced concrete floors with fire-retardant coating by calculation and experimental method. E3S Web of Conferences, 60, 00003. doi: https://doi.org/10.1051/e3sconf/20186000003
  9. Reproduced with permission from fire loss in the United States during 2019 (2020). National Fire Protection Association.
  10. Ragimov, S., Sobyna, V., Vambol, S., Vambol, V., Feshchenko, A., Zakora, A. et. al. (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, 1 (91), 27–33. doi: https://doi.org/10.5604/01.3001.0012.9654
  11. Otrosh, Y., Semkiv, O., Rybka, E., Kovalov, A. (2019). About need of calculations for the steel framework building in temperature influences conditions. IOP Conference Series: Materials Science and Engineering, 708 (1), 012065. doi: https://doi.org/10.1088/1757-899x/708/1/012065
  12. 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 Technologies, 3 (10 (87)), 63–73. doi: https://doi.org/10.15587/1729-4061.2017.102314
  13. Kustov, M. V., Kalugin, V. D., Tutunik, V. V., Tarakhno, E. V. (2019). Physicochemical principles of the technology of modified pyrotechnic compositions to reduce the chemical pollution of the atmosphere. Voprosy Khimii i Khimicheskoi Tekhnologii, 1, 92–99. doi: https://doi.org/10.32434/0321-4095-2019-122-1-92-99
  14. 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: https://doi.org/10.15587/1729-4061.2020.210059
  15. Sadkovyi, V., Andronov, V., Semkiv, O., Kovalov, A., Rybka, E., Otrosh, Yu. et. al.; Sadkovyi, V., Rybka, E., Otrosh, Yu. (Eds.) (2021). Fire resistance of reinforced concrete and steel structures. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 180. doi: https://doi.org/10.15587/978-617-7319-43-5
  16. Pospelov, B., Andronov, V., Rybka, E., Samoilov, M., Krainiukov, O., Biryukov, I. et. al. (2021). Development 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: https://doi.org/10.15587/1729-4061.2021.226692
  17. 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: https://doi.org/10.15587/1729-4061.2017.96694
  18. Pospelov, B., Andronov, V., Rybka, E., Skliarov, S. (2017). Design of fire detectors capable of self-adjusting by ignition. Eastern-European Journal of Enterprise Technologies, 4 (9 (88)), 53–59. doi: https://doi.org/10.15587/1729-4061.2017.108448
  19. Pospelov, B., Andronov, V., Rybka, E., Skliarov, S. (2017). Research into dynamics 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: https://doi.org/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: https://doi.org/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. Algorithms, 7 (4), 523–537. doi: https://doi.org/10.3390/a7040523
  22. BS EN 54-30:2015. Fire detection and fire alarm systems. Multi-sensor fire detectors. Point detectors using a combination of carbon monoxide and heat sensors. doi: https://doi.org/10.3403/30266860u
  23. BS EN 54-31:2014. Fire detection and fire alarm system - Part 31: Multi-sensor fire detectors - Point detectors using a combination of smoke, carbon monoxide and optionally heat sensors. Available at: https://standards.iteh.ai/catalog/standards/cen/6d78459f-6378-4845-bf94-3e52a88692df/en-54-31-2014
  24. ISO 7240-8:2014. Fire detection and alarm systems – Part 8: Point-type fire detectors using a carbon monoxide sensor in combination with a heat sensor.
  25. Aspey, R. A., Brazier, K. J., Spencer, J. W. (2005). Multiwavelength sensing of smoke using a polychromatic LED: Mie extinction characterization using HLS analysis. IEEE Sensors Journal, 5 (5), 1050–1056. doi: https://doi.org/10.1109/jsen.2005.845207
  26. Chen, S.-J., Hovde, D. C., Peterson, K. A., Marshall, A. W. (2007). Fire detection using smoke and gas sensors. Fire Safety Journal, 42 (8), 507–515. doi: https://doi.org/10.1016/j.firesaf.2007.01.006
  27. Shi, M., Bermak, A., Chandrasekaran, S., Amira, A., Brahim-Belhouari, S. (2008). A Committee Machine Gas Identification System Based on Dynamically Reconfigurable FPGA. IEEE Sensors Journal, 8 (4), 403–414. doi: https://doi.org/10.1109/jsen.2008.917124
  28. Skinner, A. J., Lambert, M. F. (2006). Using Smart Sensor Strings for Continuous Monitoring of Temperature Stratification in Large Water Bodies. IEEE Sensors Journal, 6 (6), 1473–1481. doi: https://doi.org/10.1109/jsen.2006.881373
  29. Cheon, J., Lee, J., Lee, I., Chae, Y., Yoo, Y., Han, G. (2009). A Single-Chip CMOS Smoke and Temperature Sensor for an Intelligent Fire Detector. IEEE Sensors Journal, 9 (8), 914–921. doi: https://doi.org/10.1109/jsen.2009.2024703
  30. Wu, Y., Harada, T. (2004). Study on the Burning Behaviour of Plantation Wood. Scientia Silvae Sinicae, 40, 131.
  31. Zhang, D., Xue, W. (2010). Effect of Heat Radiation on Combustion Heat Release Rate of Larch. Journal of West China Forestry Science, 39, 148.
  32. 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.
  33. Peng, X., Liu, S., Lu, G. (2005). Experimental Analysis on Heat Release Rate of Materials. Journal of Chongqing University, 28, 122.
  34. 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: https://doi.org/10.15587/1729-4061.2018.142995
  35. 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 Technologies, 3 (9 (93)), 34–40. doi: https://doi.org/10.15587/1729-4061.2018.133127
  36. Pospelov, B., Rybka, E., Meleshchenko, R., Krainiukov, O., Biryukov, I., Butenko, T. et. al. (2021). Short-term fire forecast based on air state gain recurrence and zero-order brown model. Eastern-European Journal of Enterprise Technologies, 3 (10 (111)), 27–33. doi: https://doi.org/10.15587/1729-4061.2021.233606
  37. Pospelov, B., Rybka, E., Krainiukov, O., Yashchenko, O., Bezuhla, Y., Bielai, S. et. al. (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: https://doi.org/10.15587/1729-4061.2021.238555
  38. Pospelov, B., Rybka, E., Togobytska, V., Meleshchenko, R., Danchenko, Y., Butenko, T. et. al. (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: https://doi.org/10.15587/1729-4061.2019.176579
  39. Pospelov, B., Rybka, E., Meleshchenko, R., Borodych, P., Gornostal, S. (2019). Development of the method for rapid detection of hazardous atmospheric pollution of cities with the help of recurrence measures. Eastern-European Journal of Enterprise Technologies, 1 (10 (97)), 29–35. doi: https://doi.org/10.15587/1729-4061.2019.155027
  40. Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Karpets, K., Pirohov, O. et. al. (2019). Development of the correlation method for operative detection of recurrent states. Eastern-European Journal of Enterprise Technologies, 6 (4 (102)), 39–46. doi: https://doi.org/10.15587/1729-4061.2019.187252
  41. Sadkovyi, V., Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Rud, A. et. al. (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 Technologies, 6 (10 (108)), 14–22. doi: https://doi.org/10.15587/1729-4061.2020.218714
  42. 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 Technologies, 2 (10 (104)), 6–12. doi: https://doi.org/10.15587/1729-4061.2020.200140
  43. Pospelov, B., Andronov, V., Rybka, E., Bezuhla, Y., Liashevska, O., Butenko, T. et. al. (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: https://doi.org/10.15587/1729-4061.2022.263194
  44. McGrattan, K., Hostikka, S., McDermott, R., Floyd, J., Weinschenk, C., Overholt, K. (2016). Fire Dynamics Simulator Technical Reference Guide. Vol. 3. National Institute of Standards and Technology. Available at: https://www.fse-italia.eu/PDF/ManualiFDS/FDS_Validation_Guide.pdf
  45. Floyd, J., Forney, G., Hostikka, S., Korhonen, T., McDermott, R., McGrattan, K. (2013). Fire Dynamics Simulator (Version 6) User’s Guide. National Institute of Standard and Technology. Vol. 1.
  46. 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
  47. Heskestad, G., Newman, J. S. (1992). Fire detection using cross-correlations of sensor signals. Fire Safety Journal, 18 (4), 355–374. doi: https://doi.org/10.1016/0379-7112(92)90024-7
  48. Gottuk, D. T., Wright, M. T., Wong, J. T., Pham, H. V., Rose-Pehrsson, S. L., Hart, S. et. al. (2002). Prototype Early Warning Fire Detection Systems: Test Series 4 Results. NRL/MR/6180–02–8602, Naval Research Laboratory.
  49. Saeed, M., Alfatih, S. (2013). Nonlinearity detection in hydraulic machines utilizing bispectral analysis. TJ Mechanical engineering and machinery, 13–21.
  50. 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 (4), 929–950. doi: https://doi.org/10.3390/a8040929
  51. Yang, B., Wang, M., Zan, T., Gao, X., Gao, P. (2021). Application of Bispectrum Diagonal Slice Feature Analysis in Tool Wear States Monitoring. Research Square. doi: https://doi.org/10.21203/rs.3.rs-775113/v1
  52. 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 (4), 1177. doi: https://doi.org/10.3390/s21041177
  53. Max, J. (1981). Principes generaus et methods classiques. Vol. 1. Paris, 311.
  54. 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
  55. Nikias, C. L., Raghuveer, M. R. (1987). Bispectrum estimation: A digital signal processing framework. Proceedings of the IEEE, 75 (7), 869–891. doi: https://doi.org/10.1109/proc.1987.13824
Peculiarities of amplitude spectra of the third order for the early detection of indoor fires

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Published

2022-10-29

How to Cite

Pospelov, B., Rybka, E., Savchenko, A., Dashkovska, O., Harbuz, S., Naden, E., Chornomaz, I., Hryshko, S., Nepsha, O., & Morkvin, D. (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. https://doi.org/10.15587/1729-4061.2022.265781