Abstract
Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.
Highlights
Introduction on back propagation (BP) Neural NetworkInformationFire warning involves the judgement of situations that are full of randomness and uncertainty and are difficult to characterize due to statistical inference
This study focused on the data characteristics of temperature, smoke and carbon monoxide (CO) in the early stage of the fire, and a multi-sensor data fusion algorithm based on a BP neural network is proposed for fire early warning
The fire training dataset was used to train these models established by each node in the interval, and the results are shown as below: From Table 1, it can be seen that BP neural network has the lowest mean squared error when the number of hidden layer nodes is six
Summary
Time is a crucial factor in fire detection [18], and available safe escape time (ASET). The key to fire early warning is to detect changes of fire characteristic parameters (like temperature, smoke concentration and CO) in the environment at the initial stage of the fire, judge whether it is a fire. There are two technical challenges: First, the materials which caused the fire are so different that the data of fire parameters at the initial stage are hard to characterize. Shorter times mean more subtle changes of fire parameters, which makes it more difficult to distinguish between fire signals and environmental disturbances. TheParameters key to fire early warning is to detect changes of fire characteristic parameters (likeIn temperature, smoke concentration and CO). Shorter times more subtle changes suitable of fire parameters, which important for firedifficult early warning algorithms. The following areand the fire parameters used in makes it more to distinguish between fire signals environmental disturbthis study: ances
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