Early stage fire detection using reliable metal oxide gas sensors and artificial neural networks

Conventional fire detectors use the smoke density or the high air temperature to trigger the fire alarm. These devices lack of ability to detect the source of fire in the early stage and they always create false alarms. In this paper, a reliable electronic nose (EN) system designed from the combination of various metal oxide gas sensors (MOGS) is applied to detect the early stage of fire from various sources. The time series signals of the same source of fire in every repetition data are highly correlated and each source of fire has a unique pattern of time series data. Therefore, the error backpropagation (BP) method can classify the tested smell with 99.6% of correct classification by using only a single training data from each source of fire. The results of the k-means algorithms can be achieved 98.3% of correct classification which also show the high ability of the EN to detect the early stage of fire from various sources accurately.