Prospects for development of intelligent fire detectors

Introduction. Advanced technologies, integrated into automatic fire fighting systems, are vital for prevention and fast detection of fires. Non-functional or malfunctioning equipment and technology often become the main cause of fire. However, trends in intelligent fire detectors enjoy insufficient attention of domestic authors. Therefore, the article focuses on this issue. Relevant tasks are solved to study automatic fire fighting systems and the role of detectors in their operation, to identify the main areas of research addressed by relevant domestic and foreign publications. The availability of accurate values and adjustability of parameters ensure high process safety and a good response time, if needed.

Analysis. Intelligent detectors, taking advantage of such tools and technologies as machine learning and electronic nose, their application in fire hazard detection are considered. Diagrams and data, describing the use of fire detectors, are presented. Different levels of safety conditions are analyzed. Methods for converting signals, coming from detectors, as well as options for integrating electronic nose techniques and thermal imaging cameras into automatic fire fighting systems are proposed. In addition, authors emphasize the benefits of electronic nose, machine vision, and micro-sensor clusters in fire safety assurance.

Conclusions. Intelligent fire detectors skyrocket the efficiency and reliability of automatic fire fighting systems. The authors’ findings will be helpful for researchers, engineers and designers of automatic fire fighting systems, students majoring in 20.03.01 Technosphere Safety, 27.03.01 Standardization and Metrology, students majoring in 27.04.02 Intelligent Measurement Systems for Quality Management, Quality Control and Product Certification.

1. Gitelman L.D., Kozhevnikov M.V. Electrification as a driver of the development of “smart cities”. The economy of the region. 2017; 13(4):1199-1210. DOI: 10.17059/2017-4-18 (rus).

2. Korolchenko D.A., Korolchenko A.Ya. Fundamentals of fire safety of an enterprise, a complete course of fire-technical minimum : textbook, 3rd ed., rev. and exp. Moscow, Pozhnauka Publ., 2011. (rus).

3. Wu T.Z. Automatic control of an intelligent building based on sensors. Prospects for the development of information technologies. 2011; 3-2: 57-60. (rus).

4. Lui Z., Kim A.K. Review of recent developments in fire detection technologies. Journal of Fire Protection Engineering. 2003; 13(2):1-20. DOI: 10.1177/104239103033496

5. Darbakov D.V., Menkeev A.I., Members A.N. The current state and prospects of development of aspiration fire detectors (API). Fire safety: problems and prospects. 2018; 1(9):202-205. (rus).

6. Jiang L. Intelligent control of building fire protection system using digital twins and semantic web technologies. Automation in construction. 2023; 147(8):104728. DOI: 10.1016/j.autcon.2022.104728

7. Hsu W.-L., Jhuang J.Y., Huang C.S., Liang C.K., Shiau Y.C. Application of internet of things in a kitchen fire prevention system. Applied Sciences. 2019; 9(3520):1-22. DOI: 10.3390/app9173520

8. Belozerov V.V. Methods, models and means of automation of technosphere safety management : abstract of the dissertation of the Doctor of Technical Sciences. Rostov-on-Don, 2012; 46. (rus).

9. Perikov A.V. System analysis and nanotechnology of safety in engineering systems of residential high-rise buildings. Nanotechnology in construction. 2018; 10(2):114-130. (rus).

10. Andreev A.V., Doronin A.S., Terekhin S.N. Prospects for building fire alarm systems based on the principles of artificial intelligence (on the example of gas fire detectors). Bulletin of the St. Petersburg University of the State Fire Service of the Ministry of Emergency Situations of Russia. 2022; 1:65-74. (rus).

11. Maxkamov B., Muminova S.S., Sidiqov S.M. o’g’li. Ensuring fire safety during the implementation of the “smart house” concept. Science and innovation. 2022; 3:338-345. DOI: 10.5281/zenodo.6819386

12. Huang X., Wu X., Usmani A. Perspectives of using artificial intelligence in building fire safety. Handbook of Cognitive and Autonomous Systems for Fire Resilient Infrastructures. Chapter 6. Naser M., Corbett G. (eds.). Springer. 2022; 1-25. DOI: 10.1007/978-3-030-98685-8_6

13. Li H., Chen Z., Lu Y., Li P., Wang Q.A., Liu Z. et al. Research on intelligent monitoring of fire safety and fire rescue plan for tunnel operation under quasi-unattended background. Buildings. 2023; 13(8):2110. DOI: 10.3390/buildings13082110

14. Martinsson J., Runefors M., Frantzich H., Glebe D., McNamee M., Mogren O. A novel method for smart fire detection using acoustic measurements and machine learning: proof of concept. Fire Technology. 2022; 58(6):3385-3403. DOI: 10.1007/s10694-022-01307-1

15. Wu Z., Wang H., Wang X., Zheng H., Chen Z., Meng C. Development of electronic nose for qualitative and quantitative monitoring of volatile flammable liquids. Sensors. 2020; 20(7):1817. DOI: 10.3390/s20071817

16. Joseph P., Bakirtzis D., Vieille A. An “electronic nose” as a potential device for fire detection of forest product fire loads in enclosures. Wood Material Science and Engineering. 2014; 10(1):130-144. DOI: 10.1080/17480272.2014.939713

17. Khorramifar A., Karami H., Lvova L., Kolouri A., Łazuka E., Piłat-Rożek M. et al. Environmental engineering applications of electronic nose systems based on MOX gas sensors. Sensors. 2023; 23(12):5716. DOI: 10.3390/s23125716

18. Scorsone E., Pisanelli A.M., Persaud K. Development of an electronic nose for fire detection. Sensors and Actuators B Chemical. 2006; 116(1-2):55-61. DOI: 10.1016/j.snb.2005.12.059

19. Stankevich T.S., Mishchuk B.R. Intelligent approach to smoke detection as a dangerous fire factor. Vestnik youth science. 2022; 1(33):1-8. DOI: 10.46845/2541-82-54-2022-1(31)-11-11 (rus).

20. Geetha S., Abhishek C.S., Akshayanat C.S. Machine vision based fire detection techniques: a survey. Fire Technology. 2021; 57:591-623. DOI: 10.1007/s10694-020-01064-z

21. Mondal M.S., Prasad V., Kumar R., Saha N., Guha S., Ghosh R. et al. Automating fire detection and suppression with computer vision: a multi-layered filtering approach to enhanced fire safety and rapid response. Fire Technology. 2023; 59(4):1555-1583. DOI: 10.1007/s10694-023-01392-w