An adaptive method for industrial hydrocarbon flame detection.

Abstract

An adaptive method for an infrared (IR) hydrocarbon flame detection system is presented. The model makes use of joint time-frequency analysis (JTFA) for feature extraction and the artificial neural networks (ANN) for training and classification. Multiple ANNs are trained independently on a computer, using the backpropagation conjugate-gradient (CG) method, with input data collected from various flame and non-flame nuisance signals at four different IR wavelengths. The trained ANN connection weights are programmed into an embedded system as part of the filtering scheme for distinguishing flames from nuisance sources. Signal saturation caused by the excessive intensity of some IR sources is resolved by an adjustable gain control mechanism. The model described herein is employed in an industrial hydrocarbon flame detector.