On-line burning state recognition for sintering process using SSIM index of flame images

Recognition of burning state based on flame images has been an important issue in sintering process of rotary kiln. Existing methods usually adopt techniques of image segmentation, pattern recognition and machine learning, which have high demands on the quality of samples and computational power. It is challenging to the on-line recognition of the burning state, which is essential in realtime control systems. This paper proposes a new approach to the burning state recognition by comparing the structural similarity (SSIM) index of flame images. The burning state is identified according to the maximum SSIM index between the real time flame image and images in two standard libraries which consist reference images with normal-burning state and under-burning state respectively. This method has low computational complexity and is suitable for online control in the rotary kiln system. Simulation results show that the proposed method achieves high recognition accuracy with low computation.

[1]  Yong Yan,et al.  Concurrent measurement of temperature and soot concentration of pulverized coal flames , 2002, IEEE Trans. Instrum. Meas..

[2]  Tianyou Chai,et al.  Flame Image-Based Burning State Recognition for Sintering Process of Rotary Kiln Using Heterogeneous Features and Fuzzy Integral , 2012, IEEE Transactions on Industrial Informatics.

[3]  R. Rosipal,et al.  Kernel Partial Least Squares for Nonlinear Regression and Discrimination , 2002 .

[4]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[5]  Tianyou Chai,et al.  Gabor filter and eigen-flame image-based burning state recognition for sintering process of rotary kiln , 2011, IEEE Conference on Decision and Control and European Control Conference.

[6]  Peng Sun,et al.  Rotary kiln flame image segmentation based on FCM and gabor wavelet based texture coarseness , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[7]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[8]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[9]  Kimura Toru,et al.  Prediction method of unburnt carbon for coal fired utility boiler using image processing technique of combustion flame , 1990 .

[10]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[11]  Tianyou Chai,et al.  Designing compact Gabor filter banks for efficient texture feature extraction , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[12]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[13]  Edward R. Vrscay,et al.  SSIM-inspired image denoising using sparse representations , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Hans Burkhardt,et al.  Multichannel image restoration based on optimization of the structural similarity index , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[15]  Robert W. Heath,et al.  Design of Linear Equalizers Optimized for the Structural Similarity Index , 2008, IEEE Transactions on Image Processing.

[16]  Carl Duchesne,et al.  Multivariate Image Analysis of Flames for Product Quality and Combustion Control in Rotary Kilns , 2006 .

[17]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.