School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China.
Sensors (Basel). 2023 Jan 11;23(2):859. doi: 10.3390/s23020859.
High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder.
高灵敏度早期火灾探测是智能建筑安全的重要前提。然而，由于初始燃烧阶段环境参数的微小变化和不规则波动，这始终是一项具有挑战性的任务。针对这一挑战，本文提出了一种基于混合特征融合的建筑物内高灵敏度早期火灾探测与预警方法。具体来说，首先选择温度、烟雾浓度和一氧化碳浓度作为主要区分属性，以指示建筑物内火灾。其次，采用传播神经网络（BPNN）和最小二乘支持向量机（LSSVM）实现混合特征融合。此外，还引入遗传算法（GA）和粒子群优化算法（PSO）分别对 BPNN 和 LSSVM 进行优化。然后，通过 D-S 证据理论融合 GA-BPNN 和 PSO-LSSVM 的输出，做出最终决策，实现高灵敏度、可靠的早期火灾探测与预警系统。最后，开发了早期火灾预警系统，实验结果表明，该方法可以有效探测不同类型和区域的火灾，包括聚氨酯泡沫火灾、酒精火灾、山毛榉闷烧和棉织织物闷烧，准确率超过 96%。
