In order to detect the electrical fire hazards of distribution boxes in heritage buildings and achieve the purpose of
electrical fire risk prevention, this paper has established a multi-sensing pyrolytic particle electrical fire early warning
analysis model based on BP neural network, and carried out the experimental verification according to the national
standard GB 14287.5 “Electrical Fire Monitoring System Part 5: Measurement Pyrolytic Particle Electrical Fire Monitor
Detector”. Firstly, according to the features and rules of high temperature pyrolysis of various electrical insulating
materials in distribution boxes, this paper analyzed correlation between different electrical insulating materials or wood
surface temperature and risk of electrical fire hazards, and a risk analysis algorithm is established to quantify electrical
fire hazards. Then, it established a BP neural network model based on the mass concentration of pyrolytic particles and
VOC gas concentration. Finally, through the pyrolysis experiments of various electrical insulating materials and wood,
this paper conducted data analysis and verified the algorithm model. The experimental results show that the established
BP neural network analysis model is effective with mean absolute percentage error of risk prediction of about 11%. The
fitting result of this model is good, and it can be applied to pyrolytic particle electrical fire detection of distribution boxes
in heritage buildings.
This paper studies the application of smoke fire detectors in religious and sacrificial places, focusing on the influence of incense burning interference on the response performance of photoelectric smoke detectors. Based on a dual wavelength photoelectric smoke detection module, the response performance of smoke fire detector is compared through the aerosol test in the smoke test tunnel, burning incense test and four kinds of standard test fire (SH) sensitivity test in combustion chamber. According to the test data, the influence of burning incense on smoke detector is analyzed.
Aiming at exiting linear temperature fire detection technology including temperature sensing cable, fiber Raman scattering, fiber Bragg grating, this paper establish an experimental platform in cable tunnel, set two different experimental scenes of the fire and record temperature variation and fire detector response time in the processing of fire simulation. Since a small amount of thermal radiation and no flame for the beginning of the small-scale fire, only directly contacting heat detectors can make alarm response and the rest of other non- contact detectors are unable to respond. In large-scale fire, the alarm response time of the fiber Raman temperature sensing fire detector and fiber Bragg grating temperature sensing fire detector is about 30 seconds, and depending on the thermocouples’ record the temperature over the fire is less than 35℃ in first 60 seconds of large-scale fire, while the temperature rising is more than 5℃/min within the range of ± 3m. According to the technical characteristics of the three detectors, the engineering suitability of the typical linear heat detectors in cable tunnels early fire detection is analyzed, which provide technical support for the preparation of norms.
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