Combustion Diagnosis Based On Color Mathematical Morphology And Fuzzy Neural Network

The main direction of boiler combustion diagnosis is by digital image detection and processing. The key point of this method is how to process the digital flame images, how to extract their characteristic parameters. Now the most important thing of boiler combustion diagnosis is to find and use new theories and methods to process the flame image, to study how to extract the characteristic parameters which can present the combustion characters, and to study how to select and merge the plenty of characteristic parameters for the different combustion diagnosis. And all these are try to develop the diagnosis system to be more accurate and more common in use.Mathematical morphology (MM) is a geometric approach to image processing that was developed as a powerful tool for shape analysis in binary and grayscale images, and more and more specialists are now working for the applications of Mathematical Morphology to process color image. It is the first time to apply Mathematical Morphology to do diagnosis of boiler combustion. The main contents of this paper are as follows:1 The basic Mathematical Morphology theories-binary and grayscale Mathematical Morphology are presented firstly. In order to extract the edge and skeleton both of binary and grayscale flame image, it is studied how to build the Structuring Elements and MM operators. Sequentially, characteristic parameters both of binary and grayscale flame images are studied.2 A new Color Mathematical Morphology-Vector Color Morphology based on vector ordering is set up. When Vector Color Morphology is applied to images, there is no loss or corruption of color information of the images. Then color Structuring Elements and MM operators are described, and color edge and color skeleton extractions are studied. Finally, characteristic parameters of color flame images are discussed. 3 The theory and procedure of adaptive fuzzy neural network-ANFIS FNN is discussed. In order to apply ANFIS to flame image processing, the re-build of their characteristic parameters are studied here. 4 The diagnosis of boiler combustion based on ANFIS FNN and the characteristic parameters both of grayscale and color flame images is studied. The results show that the diagnosis based on the characteristic parameters of grayscale flame images can recognize the ON/OFF flame image, but can't recognize the normal and abnormal flame images very correctly. On the other hand, the diagnosis based on the characteristic parameters of color flame images can recognize all the 3 flame patterns successfully: normal flame, abnormal flame and OFF flame.5 There is no need to fix the processing area to flame image by using MM, no matter if there is any shifting, curling, or glomming to the flame image.6 A detail discussion is presented about the calculation of Kolmogorov complexity index. The discussion points out the Kolmogorov complexity index is responsible to the signal frequency and amplitude. Kolmogorov complexity index to the negative pressure in boiler and the gray value of flame image shows it can forecast the pattern of combustion.7 The detail functions and flows of a actually combustion diagnosis system, which is developed for Power Station projects based on MM and ANFIS FNN, are introduced.