ANALYSIS OF THE FLAME PULSATION SIGNALS USING A SHORT-TIME FOURIER TRANSFORM

The main aim of the diagnostics of combustion process is ensuring its stability and efficiency. The most important aspect related to the monitoring of the combustion process is a non-invasive acquisition of information from flame and subsequently subjecting it for further processing. Such method of research allows to evaluate the course of the process and determine the characteristic conditions under which the combustion process is stable or not. The article presents the application of short-time Fourier transform for the analysis of flame pulsation signals. The aim of the research was to find an area especially sensitive to the change of combustion process conditions.

Research on Flame Combustion Stability Based on Neural Network

For the use of boiler flame image analysis to detect the boiler flame combustion stability, when the combustion affected by coal, peaking , improper operation or other effects, the flame appeared short pulsation. In general, the traditional detection methods based on gray scale variance can not avoid the impact of flame pulsation on account of the inaccuracy of the boiler combustion stability detection. This paper presents a flame combustion instability detection method based on neural network and selects multiple features which are directly related to the flame stability as neural network input vector. Experiments show that this method can fight off the tiny ripple influence caused by the impurities combustion or peak and simultaneously, greatly improve the detection accuracy and stability.