Early Fire Detection: A New Indoor Laboratory Dataset and Data Distribution Analysis

Fire

Abdul-Halim Jallad¹  Hamad Alhebsi²  Maen Takruri²  Amril Nazir³  Husam Mosleh⁴ 

[1] Department of Electrical Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khaimah P.O. Box 10021, United Arab Emirates;Department of Information Systems and Technology Management, Zayed University, Abu Dhabi P.O. Box 144534, United Arab Emirates;Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland;

关键词: early fire detection;    indoor fire analysis;    non-image fire analysis;    realistic indoor experiments;    machine learning;   

DOI  :  10.3390/fire5010011

来源: DOAJ

【 摘 要 】

Fire alarm systems are typically equipped with various sensors such as heat, smoke, and gas detectors. These provide fire alerts and notifications of emergency exits when a fire has been detected. However, such systems do not give early warning in order to allow appropriate action to be taken when an alarm is first triggered, as the fire may have already caused severe damage. This paper analyzes a new dataset gathered from controlled realistic fire experiments conducted in an indoor laboratory environment. The experiments were conducted in a controlled manner by triggering the source of fire using electrical devices and charcoal on paperboard, cardboard or clothing. Important data such as humidity, temperature, MQ139, Total Volatile Organic Compounds (TVOC) and eCO₂ were collected using sensor devices. These datasets will be extremely valuable to researchers in the machine learning and data science communities interested in pursuing novel advanced statistical and machine learning techniques and methods for developing early fire detection systems. The analysis of the collected data demonstrates the possibility of using eCO₂ and TVOC reading levels for early detection of smoldering fires. The experimental setup was based on Low-Power Wireless Area Networks (LPWAN), which can be used to reliably deliver fire-related data over long ranges without depending on the status of a cellular or WiFi Network.

【 授权许可】

Unknown