The existing
tunnel fire alarms are relatively single in detection and limited during
measurement, which leads to false alarms in the system. As a result, a multisensor
gradient data fusion model is employed to monitor tunnel fires. An intermediate
station is designed between the sensor and the fusion center, the data
information collected by the temperature, flame, and smoke sensors are uploaded
to the intermediate station for preprocessing, and the correlation function is
used to delete the data with low sensor support. Then, the multisource data from the same sensor is locally fused by the least
squares method at the intermediate station, and the optimal fusion data is
obtained. Moreover, the DempsterShafer evidence theory
algorithm is used to transmit these optimal fusion data to the fusion center
for global fusion to obtain the final fusion value, and the current occurrence
of fire in the tunnel is determined by judging the output results. The final
results do not depend on a single sensor or monitoring point data. Through the
experimental simulation of some data of the existing tunnel fire detectors, the
experimental results show that, compared with the traditional method, the
tunnel fire monitoring and alarm system based on multisensor
data fusion can more accurately identify the fire occurrence in the tunnel.
YU Lingfeng
. Tunnel Fire Monitoring and Alarm Technology Based on
MultiSensor Data Fusion[J]. Tunnel Construction, 2022
, 42(S2)
: 261
-266
.
DOI: 10.3973/j.issn.2096-4498.2022.S2.032
