Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural netw...

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Veröffentlicht in:Information (Basel) Jg. 12; H. 2; S. 59
Hauptverfasser: Wu, Lesong, Chen, Lan, Hao, Xiaoran
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.01.2021
Schlagworte:
Accuracy
Alarm systems
Algorithms
Back propagation
Back propagation networks
back propagation neural network
Burning rate
Carbon dioxide
Carbon monoxide
Data integration
False alarms
Fire alarms
Fire detection
fire detection time
Heat
Humidity
Internet of Things
multi-sensor data fusion
Multisensor fusion
Neural networks
Propagation
Smoke detectors
trend extraction
United States > US
ISSN:2078-2489, 2078-2489
Online-Zugang:Volltext
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Zusammenfassung:Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2078-2489
2078-2489
DOI:10.3390/info12020059