preprints.org > engineering > control and systems engineering > doi: 10.20944/preprints202408.0573.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 7 August 2024 / Approved: 8 August 2024 / Online: 8 August 2024 (08:41:12 CEST)

unnel 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 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 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 four fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that the proposed improved DS evidence theory method detects the probability of fire occurrence in the four scenarios as 67.5%, 71.0%, 82.8%, and 83.5%, respectively, and identifies fire occurrence in approximately 2.4 seconds, an improvement of 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.

Tunne; Fire Detection; Multi-Sensor Data Fusion; DS Evidence TheoryDS; PyroSim

Engineering, Control and Systems Engineering

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