A Tunnel Fire Detection Method Based on an Improved Dempster-Shafer Evidence Theory.

Sensors (Basel)

Shaanxi Transportation Holding Group Co., Ltd., Xi'an 710075, China.

Published: October 2024

AI Article Synopsis

  • - This paper presents a method for detecting tunnel fires using an enhanced version of Dempster-Shafer (DS) evidence theory, aimed at improving multi-sensor data fusion while addressing inconsistencies and ambiguities in sensor readings, such as temperature and smoke concentration.
  • - A two-level data fusion framework is implemented: the first level focuses on feature fusion to filter out conflicting data and calculate basic probability assignments (BPA), while the second level assesses and normalizes evidence conflict to optimize the final BPA.
  • - The results demonstrate a significant improvement in fire detection probabilities, achieving up to 84.1% accuracy within 2.4 seconds, offering a better alternative to traditional methods and reinforcing the method’s effectiveness for enhancing safety in tunnel

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11479314PMC
http://dx.doi.org/10.3390/s24196455DOI Listing

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