The electronic nose system can identify and detect the toxic gases released by flammable materials duringcombustion. This paper studies the application of the e-nose technology in the early fire detection offlammable materials. Based on the brief introduction of the e-nose system and the associated patternrecognition technology, this paper builds a high-throughput flammable material fire simulation platform, andthen, with the PVC material of the wires and cables in the electrical equipment which are prone to fire as anexample, it detects the volatile substances within the safe operating temperature range (100-180°C) and atthe warning temperature point (200°C) during the combustion process. In order to verify the anti-interferenceability of the platform, this paper selects liquor and cigarette as interferences, which are also subject todetection in the experiment. It uses discriminant function analysis (DFA) and the BP neural network method toperform statistical analysis of the collected data and the results show that both methods have gooddiscriminant effects. At the same time, it also optimizes the sensor array by the load analysis method. Throughcomparison and analysis, it is found that the eight-sensor array has a better discriminant effect. The researchresults show that the electronic nose technology can realize the early detection of flammable materials.
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