Intelligent Fire Alarm System Based on MSA System
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摘要: 针对传统的采用单一火灾传感器的自动消防系统易产生误报警、漏报警的问题,提出基于MSA(multi-step-alarm)的智能火灾报警系统.该系统采用基于BP神经网络的多传感器数据融合技术融合各种火灾传感器的信号,以减少报警时间、误报警和漏报警.提出了相关判定规则,以确认火灾报警信号,克服BP算法容易陷入误差局部最小、单纯使用训练值会产生振荡的缺陷.实验结果表明,与3种单一传感器的平均值比较,基于MSA的智能火灾报警系统的误报率和漏报率分别减少了80%和92%,报警时间缩短了50%.Abstract: An automatic fire alarm system based on MSA(multi-step-alarm) was proposed to overcome the shortcomings of a conventional fire alarm system composed of single type sensors.The proposed system integrates the signals from all fire alarm sensor by a multi-sensor data fusion system that is based on an BP neural network to reduce the possibility of failure for correct alarming.Criteria were presented to further confirm the alarm signals and to overcome the local optimum and oscillation in output of the BP neural network based solely on training.Experimental results show that,compared with the averaged results of 3 single type fire alarm sensors,the proposed system reduces false alarm by 80%,failure for alarming by 92%,and alarm time by 50%.
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Key words:
- fire /
- detection /
- alarm /
- MSA /
- multi-sensor data fusion /
- BP neural network
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