Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building

TL;DR

This paper presents a multi-stage feature selection and PCA-based classification approach for early fire detection using low-cost sensors measuring odor profiles, achieving high accuracy despite environmental variations.

Contribution

It introduces a novel multi-stage feature selection combined with PCA for improved early fire classification using sensor odor data.

Findings

Enhanced classification accuracy with PCA-based dimension reduction.

Robust detection performance across environmental variations.

Effective use of hybrid PCA-PNN classifier on sensor data.

Abstract

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.