Soft sensor for the compaction density of powders in the elongated metal tube based on Gaussian process regression

摘要

The Online measurement of compaction density of powders in the elongated metal tube is typically unavailable due to the limited conditions. To solve this problem, a soft sensor model based on Gaussian process regression method is applied, analyzing the factors that influence the powder density in the compaction process. Compared with Bayesian linear regression and SVM methods, the predicted results show that the proposed soft sensor based on Gaussian process regression model has advantage in predicting the compaction density of powders in the elongated metal tube. With this model, the real-time monitoring and control of compaction density of powders could be satisfied, which could guarantee the final explosive quality of powders in the metal tube.