Wang Haiying, Shi Yuke, Chen Long, Zhang Xiaofeng
Key Laboratory of Road Construction Technology and Equipment of Ministry of Education, Chang'an University, Xi'an 710064, China.
Shaanxi Transportation Holding Group Co., Ltd., Xi'an 710075, China.
Sensors (Basel). 2024 Oct 6;24(19):6455. doi: 10.3390/s24196455.
Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in the DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second-level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes the BPA to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming six fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that there is a 67.5%, 83.5%, 76.8%, 83%, 79.6%, and 84.1% probability of detecting fire when it occurs, respectively, and identifies fire occurrence in approximately 2.4 s, an improvement from 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.
隧道火灾通常使用各种传感器进行检测，包括测量温度、一氧化碳浓度和烟雾浓度。为了解决多传感器数据中的模糊性和不一致性问题，本文提出了一种基于改进的Dempster-Shafer（DS）证据理论的多传感器数据融合隧道火灾检测方法。为了解决DS理论中的证据冲突问题，采用了两级多传感器数据融合框架。第一级融合涉及对同一类型传感器数据的特征融合，去除模糊数据以获得特征数据，并通过特征区间计算基本概率分配（BPA）函数。第二级融合从BPA导出基本概率数，计算证据冲突程度，对BPA进行归一化以获得相对冲突程度，并使用信任系数对BPA进行优化。然后使用经典的DS证据理论进行整合，得到隧道火灾发生的概率。设置了不同的热释放率、隧道风速和火灾位置，形成六种火灾场景。使用改进的DS证据理论对每个模拟条件下的传感器监测数据进行提取和融合。结果表明，火灾发生时检测到火灾的概率分别为67.5%、83.5%、76.8%、83%、79.6%和84.1%，并且在大约2.4秒内识别出火灾发生，比传统方法提高了64.7%至70%。这证明了所提方法的可行性和优越性，突出了其在确保人员安全方面的重要意义。
