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

Sensors

Shaharil Mad Saad¹  Allan Melvin Andrew¹  Ali Yeon Md Shakaff¹  Ammar Zakaria¹ 

[1] Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Jejawi, Arau, Perlis 02600, Malaysia;

关键词: electronic nose;    gas sensors;    fire detection;    feature selection;    feature fusion;    normalized data;    Principal Component Analysis (PCA);    Probabilistic Neural Network (PNN);   

DOI  :  10.3390/s16010031

来源: DOAJ

【 摘 要 】

In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or “smellprint” emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odour profile data from five common fire sources and three common building construction materials were used to develop the classification model. Normalised feature extractions of the smell print data were performed before subjected to prediction classifier. These features represent the odour 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.

【 授权许可】

Unknown