ABOUT THE OPPORTUNITY OF WET SELF-GRINDING MILL FILLING DEGREE EVALUATION AS A RESULT OF NEURAL NETWORK SPECTRUM ANALYSIS OF ITS PIN VIBROACCELERATION SIGNAL
The question about modern intelligent information processing methods usage for ball mill filling degree evaluation is considered. Vibroacceleration signal has been measured on a semi-industrial mill for that purpose under the conditions of different ball loading. It is made with accelerometer, attached to a mill pin. A training set for a neural network is formed with the help of spectral analysis methods, applied to the obtained signal. Trained neural network is able to find the correlation between mill pin vibroacceleration signal and mill filling degree. It is found that the neural network is much more insensitive to noise disturbance, caused by ball loading change, for ball mill filling estimation in comparison with vibro-acoustic analyzer.