Year: 2025
Type: conference-paper
Abstract: This study aims to develop a real-time gas leak detection system for application in gas cylinder filling machines. To promptly recover gas during leakage incidents, the efficiency of the gas filling process was improved by reducing resource wastage. The system utilized a Raspberry Pi with a camera for image-based detection and employed the dark channel prior method to detect the presence of gas. The message queue system was used for the real-time data transmission of gas leak status, temperature, and humidity data. The system sent data to a central server via message queuing telemetry transport (MTQQ). Node-RED was used for data visualization and anomaly alerts. Machine learning methods such as support vector machines (SVMs) and decision trees were applied to analyze the correlation between gas leaks and other environmental parameters to predict leak incidents. This system effectively detected gas leakage and transmitted and analyzed the data, significantly improving the operational efficiency of the gas cylinder filling process.
Authors: Nian-Ze Hu, Hao-Lun Huang, Chun-Min Tsai, Yen-Yu Wu, You-Xin Lin, Chih-Chen Lin, Po-Han Lu
Institution: National Formosa University
Language: English
Topic: Arduino and IoT Applications
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410480518