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Research on fire detection algorithm of equipment compartment under EMU based on multi-parameter fusion
Publikationstyp
Conference Paper
Date Issued
2025-04-02
Sprache
English
Author(s)
First published in
Number in series
1394
Volume
1394
Start Page
179
End Page
188
Citation
International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Springer
ISBN of container
978-981963976-2
The smoke detection technology has high false alarm rate and is greatly affected by ambient wind speed. It is difficult for the temperature detector to realize early alarm, and there are risks of missing alarm and delayed alarm. Composite fire detection technology is an effective means to solve the above problems. In the application of composite fire detection algorithm, there are some problems such as false positives in frame-by-frame state judgment, delay alarm and fuzzy alarm time caused by unclear setting of detection interval length. In this paper, 7 standard combustibles in the equipment compartment under vehicle were tested on the fire detection simulation test platform, and typical fire characteristic parameters such as ambient temperature, CO concentration, VOC concentration and smoke concentration were obtained under 3~6 m/s horizontal wind field. Through ensemble learning and LSTM algorithm, the following conclusions are obtained: The detection interval length with the highest accuracy of state recognition is 30~40 s; The state determination algorithm model based on Xgboost - LSTM algorithm is constructed. Increased to 92.56%, the model can efficiently learn sample experimental data and accurately judge the environmental state; Within the detection interval length, the accurate alarm rate of smoulder is 78%, the accurate alarm rate of open flame is 96%, the false alarm rate and false alarm rate are 4% and 2%. The wavelet transform algorithm can judge the fire state within the detection interval length and achieve accurate alarm.
Subjects
Algorithm
Equipment Compartment under Vehicle
Fire Detection
DDC Class
620: Engineering