Compact electronic nose systems using metal oxide gas sensors for fire detection systems

In this paper, a reliable electronic nose (EN) system designed from the combination of various metal oxide gas sensors (MOGS) is applied to the detection of fire resulting from various sources at an early stage. The time series signals obtained from the same source of fire are highly correlated, and different sources of fire exhibit unique patterns in the time series data. Therefore, the error back propagation (BP) method can be effectively used for the classification of the tested smell. The accuracy of 99.6% is achieved by using only a single training data set from each source of fire. The accuracy achieved with the k-means algorithm is 98.3%, which also shows the high ability of the EN in detecting the early stage of fire from various sources.