  In this study, an early fire detection algorithm has been proposed based on
low cost array sensing system, utilizing gas sensors, dust particles and
ambient sensors such as temperature and humidity sensor. The odor or
smell-print emanated from various fire sources and building construction
materials at early stage are measured. For this purpose, odor profile data from
five common fire sources and three common building construction materials were
used to develop the classification model. Normalized feature extractions of the
smell print data were performed before subjected to prediction classifier.
These features represent the odor signals in the time domain. The obtained
features undergo the proposed multi-stage feature selection technique and
lastly, further reduced by Principal Component Analysis (PCA), a dimension
reduction technique. The hybrid PCA-PNN based approach has been applied on
different datasets from in-house developed system and the portable electronic
nose unit. Experimental classification results show that the dimension
reduction process performed by PCA has improved the classification accuracy and
provided high reliability, regardless of ambient temperature and humidity
variation, baseline sensor drift, the different gas concentration level and
exposure towards different heating temperature range.
