Fires are considered one of the most devastating accidents that could occur within a building. However, existing fire detection systems result in late fire detection or produce a high false alarm rate due to the complex patterns and temporal dynamics found in sensor signals that come from the various types of fires. This paper develops a novel fire detection algorithm that is based on multiple sensors in different locations to provide reliable real-time fire monitoring. The proposed fire detection algorithm takes into account both current and past sensor measurements and evaluates the similarity of sensor signals based on a dynamic time warping distance measure. Moreover, the proposed algorithm adaptively selects the sensors that are critical in detecting a fire in an early stage based on the developed $k$-out-of-$P$ fire voting rule, which effectively combines decision rules from $P$ multichannel sensor signals. A case study was performed using a real-life fire dataset obtained from the National Institute of Standards and Technology to demonstrate the effectiveness of the proposed approach.

火灾被认为是建筑物内可能发生的最具破坏性的事故之一。然而，由于来自各种火灾的传感器信号中存在复杂的模式和时间动态，因此现有的火灾探测系统会导致较晚的火灾探测或产生较高的误报率。本文开发了一种新颖的火灾探测算法，该算法基于位于不同位置的多个传感器来提供可靠的实时火灾监控。提出的火灾探测算法考虑了当前和过去的传感器测量值，并基于动态时间扭曲距离测量值来评估传感器信号的相似性。此外，基于已开发的算法，自适应算法选择了对早期发现火灾至关重要的传感器$ķ$-在......之外-$P$ 消防投票规则，有效地结合了决策规则 $P$多通道传感器信号。使用从美国国家标准技术研究院获得的真实火灾数据集进行了案例研究，以证明所提出方法的有效性。