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《Power Engineering》 2004-06
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Research on Flame Detection and Combustion Diagnosis Based on Spectrum Analysis and with Self Organized Neural Networks

MA Jun,YU Yue-feng,FAN Hao-jie (College of Machanical and Power Engineering,Jiaotong University, Shanghai 200204, China)  
A combustion diagnosis method for the diagnosis of combustion, based on combined FFT transformation and mode identification with self-organised neural networks is being presented, which has been used after the acquisition of test date from the lab's coal firing furnace.First,a series of flame intensity data ,acquired via an optoelectronic sensor and which flectuate with a certain frequency around some mean value ,are converted from the time plane to the frequency plane by a FFTprogram.Because remarkable difference exists between converted low frequency components of stable and those of unstable combustion,the first lowest 30 ones of each flame's power spectrum are picked out to be used as the neural network's input signals for training porpose.By self-organised training the network builds up distinct output maps corresponding to stable and unstable flame state signals.Verification shows that this method can very efficiently distinguish unstable from stable combustion conditions.Potant probing work has been done concerning choice of frequency sampling and improvement of neural network arithmetic.Figs 4,tables 3 and refs 12.
【CateGory Index】: TK227
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