4.5 Article

Odor Recognition of Thermal Decomposition Products of Electric Cables Using Odor Sensing Arrays

期刊

CHEMOSENSORS
卷 9, 期 9, 页码 -

出版社

MDPI
DOI: 10.3390/chemosensors9090261

关键词

GC materials; carbon black; odor sensor; artificial olfaction; chemical sensing; sensor array; decomposition of electric cable; safety devices; odor discrimination; machine learning

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An odor sensing system using chemosensitive resistors was employed to detect gases emitted from overheated cables in order to prevent fires. The study examined three different types of cables with various insulation materials and analyzed their thermal decomposition products using GC-MS. The odor sensing system, equipped with two arrays, achieved a high accuracy rate in distinguishing cable samples at 270 degrees C.
An odor sensing system with chemosensitive resistors was used to identify the gases generated from overheated cables to prevent fire. Three different electric cables for a distribution cabinet were used. The cables had an insulation layer made of polyvinyl chloride (PVC) or crosslinked polyethylene (XLPE). The heat resistance of the cables was tested by differential thermal and thermogravimetric analyses. The thermal decomposition products of the cables were investigated by gas chromatography-mass spectrometry (GC-MS). For the odor sensing system, two types of 16-channel array were used to detect the generated gases. One contains high-polarity GC stationary phase materials and the other contains GC stationary phase materials of high to low polarity. The system could distinguish among three cable samples at 270 degrees C with an accuracy of about 75% through both arrays trained with machine learning. Furthermore, the system could achieve a recall rate of 90% and a precision rate of 70% when the abnormal temperature was set above the cables' allowable conductor temperature at 130 degrees C. The odor sensing system could effectively detect the abnormal heating of the cables before the occurrence of fire. Therefore, it is helpful for fire prediction and detection systems in factories and substations.

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