Coal mass estimation of the coal mill based on two-step multi-sensor fusion

In the fossil power plant, it is rather difficult to measure the coal mass of the coal mill exactly, in order to make the coal mill work on the optimal active state, multi-sensor are used to fuse multiple signal, and the qualitative estimation of the coal mass is gotten from the algorithm. The neural network has the ability of self-organize, self-learn, and disposing the nonlinear problems, strong fault tolerant and robustness, D-S evidential theory can solve the uncertainty problem, but the evident is hard to get. Two-step fusion method combined the merit of the neural network and the evidential theory, the neural network is on the first step, the second step uses the normalization result as the evident. When this algorithm is simulated on the computer, the result proves that the method can estimate the coal mass qualitatively, according to the historical record of coal mill.