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

Author First name, Last name, Institution

Amril Nazir, Zayed University
Husam Mosleh, Silesian University of Technology
Maen Takruri, American University of Ras Al Khaimah
Abdul Halim Jallad, United Arab Emirates University
Hamad Alhebsi, American University of Ras Al Khaimah

ORCID Identifiers

0000-0001-7116-7607

0000-0001-9785-3920

Document Type

Article

Source of Publication

Fire

Publication Date

2-1-2022

Abstract

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.

DOI Link

10.3390/fire5010011

Publisher

MDPI AG

Volume

5

Disciplines

Computer Sciences

Keywords

Early fire detection, Indoor fire analysis, Machine learning, Non-image fire analysis, Realistic indoor experiments

Scopus ID

85123797040

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Nazir, Amril; Mosleh, Husam; Takruri, Maen; Jallad, Abdul Halim; and Alhebsi, Hamad, "Early Fire Detection: A New Indoor Laboratory Dataset and Data Distribution Analysis" (2022). All Works. 4853.
https://zuscholars.zu.ac.ae/works/4853

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series