Siamese-inception network based burner flame condition monitoring
The real-time monitoring of the flame in the furnace of coal-fired power plants is crucial for both the economics of power generation and the safe operation of the boiler.Traditional fire detection techniques based on energy signals such as light,heat and radiation can only detect the presence or absence of flame.These techniques are gradually unable to meet the increasingly stringent requirements for fine-grained"peaking"of thermal power generation.In this study,the features of flame images from actual power plants were analyzed from multiple perspectives.By leveraging an improved version of the Inception deep convolutional neural network(DCNN)for flame state classification,the multi-dimensional characteristics of flame were extracted.And a dataset was made by in-depth analysis of the flame image characteristics of the burner.At the same time,the preprocessed images of different categories of flames were used to create a flame image dataset.The Inception DCNN models were constructed to achieve flame state classification based on automatic feature extraction.It was proposed to classify the flame state of the burner based on the Siamese-Inception DCNN.It was found that the improved Siamese-Inception DCNN model,which converted the flame state classification problem into an evaluation of state similarity,was proposed indirectly to achieve the classification objective.The recognition accuracy of the network architecture reached 99.86%.
burner flame state monitoringcoal-fired power plantsInception convolutional neural networksiamese network
国家科技期刊平台
NSTL
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