Development of a Model Fire Detection Algorithm

Abstract

An adaptive model algorithm for fire (flickering flame, in the infrared region) detection is presented. The model made use of a Pyro-electric infrared sensor (PIR)/Passive Infrared Detector (PID) for infrared fire detection. Sample analog signals (around flame flicker region) were generated and simulated within the framework of the modeled PIR sensor/PID. A Joint Time Frequency Analysis (JTFA) function, specifically the Discrete Wavelet Transform (DWT) was applied to model the Digital Signal Processing (DSP) of the generated signals. It was implemented as wavelet analyzer filters for “fire” and “non-fire” feature extraction. A Piecewise Modified Artificial Neural Network (PMANN) and the Intraclass Correlation Coefficient (ICC) were employed in the decision rule. The PMANN generated polynomials which analyzed and ‘memorized’ the signals from DSP. The ICC further categorized cases as 'fire' or 'non-fire' by comparing data from the PMANN analyzed. The results show that the model algorithm can be implemented in modern day fire detectors and be used to detect industrial hydrocarbon fires with fewer false alarms than smoke detectors or ultraviolet detectors.