Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Fire Detectors Based on Chemical Sensor Arrays and Machine Learning Algorithms: Calibration and Test
Programa de Doctorat en Enginyeria i Ciències Aplicades ; In some types of fire, namely, smoldering fires or involving polymers without flame, gases and volatiles appear before smoke is released. Most of the fatalities registered for fires, are caused due to the intoxication of the building occupants over the burns. Nowadays, conventional fire detectors are based on the detection of smoke or airborne particles. In smoldering fires situations, conventional fire detectors triggers the alarm after the release of toxic emissions. The early emission of gas in fires opens the possibility to build fire alarm systems with shorter response times than widespread smoke-based detectors. Actually, the sensitivity of gas sensors to combustion products has been proved for many years. However, already early works remarked the challenge of providing reliable fire detection using chemical sensors. As gas sensors are not specific, they can be calibrated to detect large variety of fire signatures. But, at the same time, they are also potentially sensitive to any activity that releases volatiles when being performed. Cross-sensitivity to water vapor and other chemical compounds make gas-based fire alarm systems prone to false positives. For that reason, the development of reliable and robust fire detectors based on gas sensors relies in pattern recognition and Machine Learning algorithms to discriminate fire from nuisance sensor signatures. The presented PhD. Thesis explore the role of pattern recognition algorithms for fire detection using detectors based exclusively in chemical sensors. Two prototypes based on different types of gas sensors were designed. The sensor selection was performed to be sensitive to combustion products and to capture other volatiles that may help to discriminate fire and nuisances. Machine Learning algorithms for the prediction of fire were trained using standard fire tests stablished in EU norm 54. Additionally to those test experiments that may induce false alarms were also performed. Two approaches of ...
Fire Detectors Based on Chemical Sensor Arrays and Machine Learning Algorithms: Calibration and Test
Programa de Doctorat en Enginyeria i Ciències Aplicades ; In some types of fire, namely, smoldering fires or involving polymers without flame, gases and volatiles appear before smoke is released. Most of the fatalities registered for fires, are caused due to the intoxication of the building occupants over the burns. Nowadays, conventional fire detectors are based on the detection of smoke or airborne particles. In smoldering fires situations, conventional fire detectors triggers the alarm after the release of toxic emissions. The early emission of gas in fires opens the possibility to build fire alarm systems with shorter response times than widespread smoke-based detectors. Actually, the sensitivity of gas sensors to combustion products has been proved for many years. However, already early works remarked the challenge of providing reliable fire detection using chemical sensors. As gas sensors are not specific, they can be calibrated to detect large variety of fire signatures. But, at the same time, they are also potentially sensitive to any activity that releases volatiles when being performed. Cross-sensitivity to water vapor and other chemical compounds make gas-based fire alarm systems prone to false positives. For that reason, the development of reliable and robust fire detectors based on gas sensors relies in pattern recognition and Machine Learning algorithms to discriminate fire from nuisance sensor signatures. The presented PhD. Thesis explore the role of pattern recognition algorithms for fire detection using detectors based exclusively in chemical sensors. Two prototypes based on different types of gas sensors were designed. The sensor selection was performed to be sensitive to combustion products and to capture other volatiles that may help to discriminate fire and nuisances. Machine Learning algorithms for the prediction of fire were trained using standard fire tests stablished in EU norm 54. Additionally to those test experiments that may induce false alarms were also performed. Two approaches of ...
Fire Detectors Based on Chemical Sensor Arrays and Machine Learning Algorithms: Calibration and Test
22.09.2020
TDX (Tesis Doctorals en Xarxa)
Hochschulschrift
Elektronische Ressource
Englisch
DDC:
624
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