Research on risk prediction techniques of multi-sensing pyrolytic particle electrical fire in heritage buildings based on BP neural network

Abstract

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.