Real-Time Detection and Process Status Integration System for High-Pressure Gas Leakage
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 f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/92/1/72 |
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| author | Nian-Ze Hu Hao-Lun Huang Chun-Min Tsai Yen-Yu Wu You-Xin Lin Chih-Chen Lin Po-Han Lu |
| author_facet | Nian-Ze Hu Hao-Lun Huang Chun-Min Tsai Yen-Yu Wu You-Xin Lin Chih-Chen Lin Po-Han Lu |
| author_sort | Nian-Ze Hu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-506046cd273c47e8879edf785bbdab80 |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-506046cd273c47e8879edf785bbdab802025-08-20T02:21:09ZengMDPI AGEngineering Proceedings2673-45912025-05-019217210.3390/engproc2025092072Real-Time Detection and Process Status Integration System for High-Pressure Gas LeakageNian-Ze Hu0Hao-Lun Huang1Chun-Min Tsai2Yen-Yu Wu3You-Xin Lin4Chih-Chen Lin5Po-Han Lu6Smart Machinery and Intelligent Manufacturing Research Center, National Formosa University, Yunlin 632301, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanThis 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.https://www.mdpi.com/2673-4591/92/1/72gas leak detectiondark channel priormachine learningreal-time monitoringMQTT |
| spellingShingle | Nian-Ze Hu Hao-Lun Huang Chun-Min Tsai Yen-Yu Wu You-Xin Lin Chih-Chen Lin Po-Han Lu Real-Time Detection and Process Status Integration System for High-Pressure Gas Leakage Engineering Proceedings gas leak detection dark channel prior machine learning real-time monitoring MQTT |
| title | Real-Time Detection and Process Status Integration System for High-Pressure Gas Leakage |
| title_full | Real-Time Detection and Process Status Integration System for High-Pressure Gas Leakage |
| title_fullStr | Real-Time Detection and Process Status Integration System for High-Pressure Gas Leakage |
| title_full_unstemmed | Real-Time Detection and Process Status Integration System for High-Pressure Gas Leakage |
| title_short | Real-Time Detection and Process Status Integration System for High-Pressure Gas Leakage |
| title_sort | real time detection and process status integration system for high pressure gas leakage |
| topic | gas leak detection dark channel prior machine learning real-time monitoring MQTT |
| url | https://www.mdpi.com/2673-4591/92/1/72 |
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