Improved pattern amendment inversion algorithm for dust fast real-time measurement

Due to the low reliability and the bad real-time performance for coal dust concentration measurement, an improved pattern amendment inversion algorithm was presented. The pattern classification was performed according to diffraction angular with dust information to meet various needs of coal mining. Simulation indicates the minimum recognition time is reduced to 0.05 times of that before. Thereupon, transitional patterns were supplemented and the precision increased markedly. But sometimes there was gross error. Therefore, the pattern amendment function was introduced and the eigenvectors of amendment patterns were worked out. A ranking method of the amendment patterns was proposed and the normalized eigenvectors of amendment patterns ranked were stored in advance. During measurement the optimal patterns were recognized in universe and amended in local area according to the principle of the minimum of variance sum. Then the dust content could be inversed with the total light ratio of signal to optimal pattern. Experiments proved the error of total dust and respiring dust declined from 6% to 2% and from 9% to 3%, respectively. It is concluded that the improved algorithm has enhanced the precision and real-time performance of dust sensor remarkably.